Triple
T37811662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Escort Carrier Task Unit 77.4.3 |
E942660
|
entity |
| Predicate | typeOfShipsIncluded |
P109622
|
FINISHED |
| Object | escort carriers |
—
|
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: escort carriers | Statement: [Escort Carrier Task Unit 77.4.3, typeOfShipsIncluded, escort carriers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfShipsIncluded Context triple: [Escort Carrier Task Unit 77.4.3, typeOfShipsIncluded, escort carriers]
-
A.
numberOfShips
Indicates the quantity of ships associated with a given entity or situation.
-
B.
shipTypeProduced
Indicates that a particular type of ship is produced, built, or manufactured by a given entity.
-
C.
boardsShip
Indicates that one entity gets onto or enters a ship as a passenger or crew member.
-
D.
shipClass
Indicates the classification or type category to which a particular ship belongs.
-
E.
typicalShipTypes
chosen
Indicates that the subject is commonly or characteristically associated with the specified types or categories of ships.
- 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_69f76ee8104c8190ab17133ccd8f86e6 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1772e48190ba738c6d11b321e2 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:19 p.m.