Triple
T1928060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | All Nippon Airways |
E40877
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | ANA |
E181734
|
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: ANA | Statement: [All Nippon Airways, shortName, ANA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ANA Context triple: [All Nippon Airways, shortName, ANA]
-
A.
ANA
ANA is the standard three-letter abbreviation used for the Anaheim Ducks, a professional ice hockey team in the National Hockey League.
-
B.
ANA
chosen
ANA is the commonly used abbreviation for the Afghan National Army, the former main land warfare branch of Afghanistan’s armed forces.
-
C.
ANE
ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
-
D.
AN
AN is the vehicle registration code used on license plates for the Ansbach district in the Middle Franconia region of Bavaria, Germany.
-
E.
ENA
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
- 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_69a8864711648190b07bed24ed76258e |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb263cdb8819084d0bda98a2a71e0 |
completed | March 7, 2026, 5:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3eb5d2c8190b5afd16a822bf0e4 |
completed | March 8, 2026, 10:10 p.m. |
Created at: March 4, 2026, 7:35 p.m.