Triple
T1190500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation MI |
E25346
|
entity |
| Predicate | involvedShipType |
P8971
|
FINISHED |
| Object | aircraft 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: aircraft carrier | Statement: [Operation MI, involvedShipType, aircraft carrier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvedShipType Context triple: [Operation MI, involvedShipType, aircraft carrier]
-
A.
shipTypeInvolved
chosen
Indicates that a particular type or class of ship is involved or participates in a specified event, situation, or relationship.
-
B.
shipInvolved
Indicates that a ship participates in, is associated with, or plays a role in a specified event or situation.
-
C.
originalShipType
Indicates the type or category of ship that an entity was originally classified or built as.
-
D.
shipClass
Indicates the classification or type category to which a particular ship belongs.
-
E.
numberOfShipsInvolved
Indicates the total count of ships that participated or were involved in a specified event or situation.
- 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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd58d8d88190b8d9c9c9de7f4e97 |
completed | March 1, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5bacc481909e8dfd5215e4711a |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:45 p.m.