Triple
T21189136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Damansky Island |
E522164
|
entity |
| Predicate | casualtiesIn1969Clash |
P35438
|
FINISHED |
| Object | dozens of soldiers killed |
—
|
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: dozens of soldiers killed | Statement: [Damansky Island, casualtiesIn1969Clash, dozens of soldiers killed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesIn1969Clash Context triple: [Damansky Island, casualtiesIn1969Clash, dozens of soldiers killed]
-
A.
casualtiesIn1980Siege
Indicates the number of people who were killed or injured during the 1980 siege.
-
B.
casualtiesUgandanSoldiers
Indicates that the relationship involves Ugandan soldiers who have been killed, wounded, or otherwise harmed as casualties in a particular event or context.
-
C.
casualtiesAssociatedWithEvent
chosen
Indicates that certain casualties (deaths or injuries) are linked to, or resulted from, a specific event.
-
D.
sustainedHeavyCasualtiesAt
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.
-
E.
casualtiesDescription
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
- 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_69e0b51061388190aa03f19700d3ef04 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e733350e588190a31467758a8afa5c |
completed | April 21, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69e5f6027c248190a170a36612bd337e |
completed | April 20, 2026, 9:46 a.m. |
Created at: April 16, 2026, 3:07 p.m.