Triple
T29272112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USS Franklin (CV-13) |
E742145
|
entity |
| Predicate | casualtiesIn1945Attack |
P110385
|
FINISHED |
| Object | Hundreds of crew killed or wounded |
—
|
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: Hundreds of crew killed or wounded | Statement: [USS Franklin (CV-13), casualtiesIn1945Attack, Hundreds of crew killed or wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesIn1945Attack Context triple: [USS Franklin (CV-13), casualtiesIn1945Attack, Hundreds of crew killed or wounded]
-
A.
casualtiesInflictedOn
Indicates that one party has caused deaths or injuries to another party as a result of a harmful event or action.
-
B.
wasBombedOn
Indicates that a location or target experienced a bombing event at a specific time or date.
-
C.
sustainedHeavyCasualtiesAt
chosen
Indicates that an entity experienced a large number of serious losses (e.g., deaths or injuries) at a specific location or during a specific event.
-
D.
wasBombedDuring
Indicates that an entity was subjected to a bombing attack during a specified event or time period.
-
E.
cityBombed
Indicates that a particular city was subjected to a bombing attack.
- 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_69f0912124d48190a046642b69407f4c |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f664e41e5c81909063c310f222946b |
completed | May 2, 2026, 8:56 p.m. |
| PD | Predicate disambiguation | batch_69f660f2e3708190ab658652bcfc04d0 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 28, 2026, 12:48 p.m.