Triple
T29164570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Killing Hour |
E739278
|
entity |
| Predicate | hasVictimPattern |
P180872
|
FINISHED |
| Object | pairs of victims left in different locations |
—
|
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: pairs of victims left in different locations | Statement: [The Killing Hour, hasVictimPattern, pairs of victims left in different locations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVictimPattern Context triple: [The Killing Hour, hasVictimPattern, pairs of victims left in different locations]
-
A.
hasVictimCount
Indicates the number of victims associated with a particular event, action, or entity.
-
B.
hasVictimRoleWith
Indicates a relationship in which one entity occupies or is assigned the role of victim in relation to another entity or event.
-
C.
hasMainVictim
Indicates that an event, action, or harmful situation primarily targets or affects a specific victim as its main subject.
-
D.
hasTargetVictims
Indicates that an action, event, or entity is directed toward or intended to affect specific victims.
-
E.
hasVictimsCount
Indicates the number of victims associated with a particular event, action, or entity.
- F. None of above. chosen
Provenance (4 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_69f07cb528fc8190a556b73990c347c8 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f757898fe48190b124dc7301672623 |
completed | May 3, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f754c484348190948d2a04ff228fb1 |
completed | May 3, 2026, 1:59 p.m. |
| PDg | Predicate description generation | batch_69f75788d40c819083bf2567b3091585 |
completed | May 3, 2026, 2:11 p.m. |
Created at: April 28, 2026, 11:49 a.m.