Triple
T5923197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Racket |
E131743
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | Racket development team |
E131743
|
NE FINISHED |
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: Racket development team | Statement: [Racket, developer, Racket development team]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Racket development team Context triple: [Racket, developer, Racket development team]
-
A.
Racket
chosen
Racket is a modern, multi-paradigm programming language in the Lisp/Scheme family, designed for language-oriented programming, scripting, and education.
-
B.
Scratch Team
Scratch Team is the official group responsible for developing and maintaining the Scratch programming platform and its related tools and extensions.
-
C.
Racket Squad
Racket Squad was an American television crime drama series from the early 1950s that focused on exposing and dramatizing real-life confidence schemes and swindles.
-
D.
Sugar Labs
Sugar Labs is a nonprofit organization that develops and maintains the Sugar learning platform, an open-source educational software environment originally created for the One Laptop per Child project.
-
E.
Chez Scheme
Chez Scheme is a high-performance, optimizing implementation of the Scheme programming language widely used for both research and production systems.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c0085a1ed08190a7e9a8b6323fd680 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03804d9808190829a418adb7864aa |
completed | March 22, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c0483e3481908e50f8b34b11a878 |
completed | March 23, 2026, 4:23 a.m. |
Created at: March 22, 2026, 4 p.m.