Triple
T6124349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | court-martial of William Calley |
E136558
|
entity |
| Predicate | numberOfMurderCounts |
P61476
|
FINISHED |
| Object | 22 |
—
|
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: 22 | Statement: [court-martial of William Calley, numberOfMurderCounts, 22]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMurderCounts Context triple: [court-martial of William Calley, numberOfMurderCounts, 22]
-
A.
estimatedMurdersCommitted
Indicates an approximate count of murders that are believed or inferred to have been committed by an entity.
-
B.
numberOfMurderCharges
chosen
Indicates the count of distinct murder charges formally brought against an entity.
-
C.
numberOfVictimsKilled
Indicates the count of victims who were killed as a result of the referenced event or action.
-
D.
numberOfPeoplePressedToDeath
Indicates the number of people who were killed specifically by being pressed to death.
-
E.
allegedToHaveKilled
Indicates that one entity is claimed or accused, but not proven, to have killed 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_69c0089f851c81909e5e189a617dcff6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05c25976081909e0a40e07dff0b8a |
completed | March 22, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69c049f9ab3c81909c8ab6466f6a2935 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:14 p.m.