Triple
T2374557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USS Lexington (CV-2) |
E46165
|
entity |
| Predicate | wreckDepth |
P38776
|
FINISHED |
| Object | about 3,000 meters |
—
|
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: about 3,000 meters | Statement: [USS Lexington (CV-2), wreckDepth, about 3,000 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wreckDepth Context triple: [USS Lexington (CV-2), wreckDepth, about 3,000 meters]
-
A.
wreckDiscovery
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.
sunkBy
Indicates that one entity (typically a vessel or structure) was caused to sink or be destroyed in water by another entity.
-
D.
shipwreckEvent
Indicates an event in which a ship is destroyed, stranded, or severely damaged, typically resulting in loss or abandonment at sea or near a shoreline.
-
E.
sankOn
Indicates that one entity moved downward and became submerged or lower in level relative to another entity or reference point.
- F. None of above. chosen
Provenance (4 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_69a88a145268819083e2736cb835c696 |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abca4d89248190be7d712d5fa8382b |
completed | March 7, 2026, 6:48 a.m. |
| PD | Predicate disambiguation | batch_69abc59d82f08190b7c36982d1ae783d |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abca4c937c8190b4dfca716b868d4f |
completed | March 7, 2026, 6:48 a.m. |
Created at: March 4, 2026, 7:56 p.m.