Triple
T37298988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese aircraft carrier Chitose |
E925885
|
entity |
| Predicate | roleAtCapeEngano |
P150473
|
FINISHED |
| Object | decoy carrier |
—
|
LITERAL 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: decoy carrier | Statement: [Japanese aircraft carrier Chitose, roleAtCapeEngano, decoy carrier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleAtCapeEngano Context triple: [Japanese aircraft carrier Chitose, roleAtCapeEngano, decoy carrier]
-
A.
roleAtLuckyChap
Indicates that an entity holds or held a specific role or position at the organization LuckyChap.
-
B.
deceptionRole
chosen
Indicates a role that an entity plays in an act of deception, such as deceiver, accomplice, or target.
-
C.
roleInTheMask
Indicates that an entity has a specific role or character assignment in the context of "The Mask" (e.g., a production, story, or performance titled or themed "The Mask").
-
D.
roleInTheWay
Indicates that one entity is obstructing, hindering, or otherwise blocking another entity’s progress, action, or intended path.
-
E.
CapestranoRole
Indicates a role or function that an entity holds specifically in relation to Capestrano (such as within its context, organization, or system).
- F. None of above.
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_69f76eb0f86c819098dee07393e69ec3 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
Created at: May 3, 2026, 4:16 p.m.