Triple
T17498950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amazon Kinesis Data Analytics |
E426144
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object | Scala |
—
|
NE NERFINISHED |
How this triple was built (2 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: Scala | Statement: [Amazon Kinesis Data Analytics, supports, Scala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scala Context triple: [Amazon Kinesis Data Analytics, supports, Scala]
-
A.
Scala
Scala is a historic hilltop town on Italy’s Amalfi Coast, known for its medieval architecture, terraced landscapes, and panoramic views over the surrounding coastline.
-
B.
Scala
chosen
Scala is a high-level, statically typed programming language that unifies object-oriented and functional programming paradigms and runs on the Java Virtual Machine.
-
C.
Scala Center
Scala Center is a non-profit organization at EPFL dedicated to the stewardship, education, and open-source development of the Scala programming language and its ecosystem.
-
D.
Programming in Scala
Programming in Scala is a comprehensive, authoritative book that introduces and explains the Scala programming language, co-authored by its creator Martin Odersky.
-
E.
Scala Inc.
Scala Inc. is a company specializing in digital signage and visual communication solutions, known for developing hardware and software platforms for dynamic display networks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4521028048190aa7c4023a72a12f4 |
completed | April 19, 2026, 3:54 a.m. |
Created at: April 10, 2026, 5:48 a.m.