Triple
T17520172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dask |
E426661
|
entity |
| Predicate | supportsDataFormat |
P8463
|
FINISHED |
| Object |
Parquet
Parquet is a columnar storage file format optimized for efficient data compression and query performance, widely used in big data processing frameworks.
|
E1274314
|
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: Parquet | Statement: [Dask, supportsDataFormat, Parquet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parquet Context triple: [Dask, supportsDataFormat, Parquet]
-
A.
Norsey Wood
Norsey Wood is an ancient woodland and designated Local Nature Reserve near Billericay in Essex, known for its rich biodiversity and archaeological features.
-
B.
Wood
Wood is a common English surname with historical roots in Britain, often originally referring to someone who lived or worked near a forest.
-
C.
Maderas
Maderas is a stratovolcano on Ometepe Island in Lake Nicaragua, known for its cloud forest, crater lagoon, and popular hiking trails.
-
D.
Cherrywood
Cherrywood is a suburban area in south Dublin, Ireland, known for its modern residential and business developments and served by the Luas light rail system.
-
E.
Wood End
Wood End is a residential neighborhood located within the town of Hayes in west London, England.
- 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: Parquet Triple: [Dask, supportsDataFormat, Parquet]
Generated description
Parquet is a columnar storage file format optimized for efficient data compression and query performance, widely used in big data processing frameworks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Parquet Target entity description: Parquet is a columnar storage file format optimized for efficient data compression and query performance, widely used in big data processing frameworks.
-
A.
Norsey Wood
Norsey Wood is an ancient woodland and designated Local Nature Reserve near Billericay in Essex, known for its rich biodiversity and archaeological features.
-
B.
Wood
Wood is a common English surname with historical roots in Britain, often originally referring to someone who lived or worked near a forest.
-
C.
Maderas
Maderas is a stratovolcano on Ometepe Island in Lake Nicaragua, known for its cloud forest, crater lagoon, and popular hiking trails.
-
D.
Cherrywood
Cherrywood is a suburban area in south Dublin, Ireland, known for its modern residential and business developments and served by the Luas light rail system.
-
E.
Wood End
Wood End is a residential neighborhood located within the town of Hayes in west London, England.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d23cf08190925510344fa36f57 |
completed | April 19, 2026, 3:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01c94237d08190bb1f874735c87803 |
completed | May 11, 2026, 12:19 p.m. |
| NEDg | Description generation | batch_6a01cabea2b48190a690b17a88d45b40 |
completed | May 11, 2026, 12:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01cefa08f8819086cb86ce22193baa |
completed | May 11, 2026, 12:43 p.m. |
Created at: April 10, 2026, 5:49 a.m.