Triple
T13187498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pratt & Whitney Canada PT6T Twin‑Pac |
E313894
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | PT6T‑3B |
E588916
|
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: PT6T‑3B | Statement: [Pratt & Whitney Canada PT6T Twin‑Pac, hasVariant, PT6T‑3B]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PT6T‑3B Context triple: [Pratt & Whitney Canada PT6T Twin‑Pac, hasVariant, PT6T‑3B]
-
A.
PT6
chosen
PT6 is a widely used family of turboprop and turboshaft aircraft engines developed by Pratt & Whitney Canada, renowned for their reliability and versatility in civil and military aviation.
-
B.
T-346A
T-346A is the Italian Air Force’s designation for the M-346 Master, an advanced jet trainer and light attack aircraft developed by Leonardo.
-
C.
Tianshou
Tianshou is a reinforcement learning library for PyTorch that provides modular, efficient tools and algorithms for training and evaluating RL agents.
-
D.
Tianshou
Tianshou was the brief era name proclaimed by Empress Wu Zetian during her rule in the late 7th century Tang China.
-
E.
TMB 6000 series
The TMB 6000 series is a class of modern electric multiple-unit trains used on the Barcelona Metro system.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c4d60688190b34e65bbb5d4c152 |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6f5f7a304819081c4f51631948cbd |
completed | May 3, 2026, 7:15 a.m. |
Created at: April 9, 2026, 9:15 p.m.