Triple
T15357057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Batplane |
E367190
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Batjet |
E367190
|
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: Batjet | Statement: [Batplane, alsoKnownAs, Batjet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Batjet Context triple: [Batplane, alsoKnownAs, Batjet]
-
A.
Batplane
chosen
The Batplane is Batman’s specialized, high-tech aircraft designed for rapid aerial transport, combat, and surveillance in his crime-fighting operations.
-
B.
The Jet
The Jet was the nickname of Joe Perry, a Hall of Fame NFL fullback renowned for his speed and success with the San Francisco 49ers in the 1950s.
-
C.
Wing
Wing is an experimental mobile operating system and user interface project developed by X (formerly Google X) to explore new paradigms in smartphone interaction and design.
-
D.
Wing
Wing is an Alphabet Inc. subsidiary focused on developing and operating drone-based delivery services and related logistics technologies.
-
E.
Wing
Wing is a Japanese lingerie and intimate apparel brand known for its comfortable, everyday undergarments for women.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e2c00648190ae2325e1ee58dcfd |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b45e3048190a7fa62ead6916fed |
completed | May 9, 2026, 10:24 a.m. |
Created at: April 10, 2026, 3:18 a.m.