Triple
T37811662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Escort Carrier Task Unit 77.4.3 |
E942660
|
entity |
| Predicate | typeOfShipsIncluded |
—
|
GENERATED |
| Object | escort carriers |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
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 (1 batch)
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. |
Created at: May 3, 2026, 4:19 p.m.