Triple
T5438010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port Chicago Naval Magazine site |
E122058
|
entity |
| Predicate | numberOfInjuredInEvent |
P63693
|
FINISHED |
| Object | 390 |
—
|
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: 390 | Statement: [Port Chicago Naval Magazine site, numberOfInjuredInEvent, 390]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInjuredInEvent Context triple: [Port Chicago Naval Magazine site, numberOfInjuredInEvent, 390]
-
A.
numberOfVictimsInjured
chosen
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
B.
hasInjuredPerson
Indicates that an entity has a person who has been harmed or injured associated with it.
-
C.
injuredIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
-
D.
numberOfPeopleLaterDyingOfInjuriesConsidered
Indicates the number of people who subsequently died from injuries that were previously evaluated or taken into account.
-
E.
numberOfSuspectedVictims
Indicates the count of individuals believed or alleged to be victims in a particular incident, case, or context.
- 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_69bd46400768819092925d461c0b8432 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd922f66bc8190b7d47fd68d2fcf2e |
completed | March 20, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69bd919aeb048190b786f814177d6cd9 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:07 p.m.