Triple
T30335840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | murder of Nancy Montgomery |
E771617
|
entity |
| Predicate | occupationOfVictim |
P1786
|
FINISHED |
| Object | housekeeper |
—
|
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: housekeeper | Statement: [murder of Nancy Montgomery, occupationOfVictim, housekeeper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationOfVictim Context triple: [murder of Nancy Montgomery, occupationOfVictim, housekeeper]
-
A.
victimOccupation
chosen
Indicates the profession or job role held by the person who is the victim in an event or incident.
-
B.
typeOfVictimization
Indicates the specific kind or category of harmful act, abuse, or exploitation experienced by a victim.
-
C.
portraysAsVictim
Indicates that one entity represents or depicts another entity as a victim in a given context or narrative.
-
D.
hasTypicalVictimRole
Indicates that an entity typically occupies the role of a victim in the context of a particular action, event, or relationship.
-
E.
allegedVictimOf
Indicates that one entity is claimed or reported to have been harmed, wronged, or victimized by another entity, without asserting that the claim is proven.
- 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_69f2248aba24819095bb86480d55b23b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f69dfdda708190be290c7bec205445 |
completed | May 3, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f69d1a37e081908d1d86b90ff502bd |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 29, 2026, 7:54 p.m.