Triple
T6124369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | court-martial of William Calley |
E136558
|
entity |
| Predicate | involvesTopic |
P1256
|
FINISHED |
| Object | war crimes |
—
|
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: war crimes | Statement: [court-martial of William Calley, involvesTopic, war crimes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesTopic Context triple: [court-martial of William Calley, involvesTopic, war crimes]
-
A.
involvesIssue
Indicates that an action, event, or entity is related to, concerns, or includes a particular issue.
-
B.
involves
chosen
Indicates that an entity participates in, is a part of, or is implicated within a particular event, process, or relationship.
-
C.
frequentlyDiscussedIn
Indicates that a topic, subject, or entity is often the focus of conversation, debate, or mention within a particular context or medium.
-
D.
involvesTitle
Indicates that the relationship or action includes or makes reference to a specific title (such as a role, honorific, or formal designation).
-
E.
oftenInvolvedWith
Indicates that one entity frequently participates in or is commonly associated with activities, events, or situations involving 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.