Triple
T18205422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugging Face Datasets |
E435888
|
entity |
| Predicate | usesFormat |
P130
|
FINISHED |
| Object |
Apache Arrow
Apache Arrow is an open-source, columnar in-memory data format and computing framework designed for high-performance analytics and efficient data interchange across different systems and languages.
|
E1312494
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Apache Arrow | Statement: [Hugging Face Datasets, usesFormat, Apache Arrow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apache Arrow Context triple: [Hugging Face Datasets, usesFormat, Apache Arrow]
-
A.
Apache Parquet
Apache Parquet is a columnar storage file format optimized for efficient data compression and query performance in big data processing frameworks such as Apache Hadoop and Apache Spark.
-
B.
Apache Avro
Apache Avro is a data serialization system and file format in the Apache Hadoop ecosystem that provides compact, fast, binary data encoding with rich schema support and dynamic typing.
-
C.
Apache Iceberg
Apache Iceberg is an open table format for huge analytic datasets that enables reliable, high-performance querying and data management in data lake environments.
-
D.
Apache Tez
Apache Tez is a distributed data processing framework designed for building high-performance batch and interactive data workflows on Hadoop.
-
E.
Apache Impala
Apache Impala is a massively parallel, SQL-on-Hadoop query engine designed for low-latency, interactive analysis of large-scale data stored in distributed systems.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Apache Arrow Triple: [Hugging Face Datasets, usesFormat, Apache Arrow]
Generated description
Apache Arrow is an open-source, columnar in-memory data format and computing framework designed for high-performance analytics and efficient data interchange across different systems and languages.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Apache Arrow Target entity description: Apache Arrow is an open-source, columnar in-memory data format and computing framework designed for high-performance analytics and efficient data interchange across different systems and languages.
-
A.
Apache Parquet
Apache Parquet is a columnar storage file format optimized for efficient data compression and query performance in big data processing frameworks such as Apache Hadoop and Apache Spark.
-
B.
Apache Avro
Apache Avro is a data serialization system and file format in the Apache Hadoop ecosystem that provides compact, fast, binary data encoding with rich schema support and dynamic typing.
-
C.
Apache Iceberg
Apache Iceberg is an open table format for huge analytic datasets that enables reliable, high-performance querying and data management in data lake environments.
-
D.
Apache Tez
Apache Tez is a distributed data processing framework designed for building high-performance batch and interactive data workflows on Hadoop.
-
E.
Apache Impala
Apache Impala is a massively parallel, SQL-on-Hadoop query engine designed for low-latency, interactive analysis of large-scale data stored in distributed systems.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e2234b988190bbe2c2164d61f65f |
completed | April 19, 2026, 2:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a039f0e52108190913cc5c667619d89 |
completed | May 12, 2026, 9:43 p.m. |
| NEDg | Description generation | batch_6a039fdd9c4c819083b450657d0ece43 |
completed | May 12, 2026, 9:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03a0d6de8c8190b1f94c7de0856143 |
completed | May 12, 2026, 9:51 p.m. |
Created at: April 10, 2026, 10:32 a.m.