Triple
T571287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USS Yorktown (CV-5) |
E13667
|
entity |
| Predicate | wreckDiscovered |
P6265
|
FINISHED |
| Object | 1998 |
—
|
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: 1998 | Statement: [USS Yorktown (CV-5), wreckDiscovered, 1998]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wreckDiscovered Context triple: [USS Yorktown (CV-5), wreckDiscovered, 1998]
-
A.
wreckDiscovery
chosen
Indicates that an entity discovers, finds, or identifies a wreck (such as a ruined or destroyed object, vehicle, or structure).
-
B.
placeOfSinking
Indicates the location where an object or entity sank or was submerged.
-
C.
shipwreckedOn
Indicates that an entity becomes stranded or marooned on a particular landmass or location as a result of a shipwreck.
-
D.
sunkBy
Indicates that one entity (typically a vessel or structure) was caused to sink or be destroyed in water by another entity.
-
E.
hasTypeOfWreck
Indicates that one entity is classified as a specific type or category of wreck (e.g., shipwreck, car wreck) associated with another entity.
- 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b483ac08190b3be152a7cf42011 |
completed | March 1, 2026, 8:02 p.m. |
| PD | Predicate disambiguation | batch_69a494c2caac819086ab316fa49d324c |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.