Triple
T28103348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lumberjack Tavern |
E710294
|
entity |
| Predicate | hasVictimInCase |
P170970
|
FINISHED |
| Object | Maurice Chenoweth |
—
|
NE NERFINISHED |
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: Maurice Chenoweth | Statement: [Lumberjack Tavern, hasVictimInCase, Maurice Chenoweth]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVictimInCase Context triple: [Lumberjack Tavern, hasVictimInCase, Maurice Chenoweth]
-
A.
hasVictimCount
Indicates the number of victims associated with a particular event, action, or entity.
-
B.
isVictimOf
Indicates that one entity suffers harm, loss, or wrongdoing as a result of another entity’s actions or events.
-
C.
hasMainVictim
Indicates that an event, action, or harmful situation primarily targets or affects a specific victim as its main subject.
-
D.
hasVictimRoleWith
chosen
Indicates a relationship in which one entity occupies or is assigned the role of victim in relation to another entity or event.
-
E.
hasVictimsCount
Indicates the number of victims associated with a particular event, action, or 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_69ef9b71fdb081908b4a61cd7ff147c1 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f6db1f3ec48190a82e7d893d3c76ba |
completed | May 3, 2026, 5:20 a.m. |
| PD | Predicate disambiguation | batch_69f6d82adfa481908a5e196d2e18c73f |
completed | May 3, 2026, 5:07 a.m. |
Created at: April 27, 2026, 9:06 p.m.