Triple
T32154065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Preppy Killer |
E821232
|
entity |
| Predicate | hasVictimBackground |
P191022
|
FINISHED |
| Object | New York City teenager |
—
|
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: New York City teenager | Statement: [Preppy Killer, hasVictimBackground, New York City teenager]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVictimBackground Context triple: [Preppy Killer, hasVictimBackground, New York City teenager]
-
A.
hasPerpetratorBackground
Indicates that an action, event, or crime is associated with information describing the background or history of its perpetrator.
-
B.
hasMainVictim
Indicates that an event, action, or harmful situation primarily targets or affects a specific victim as its main subject.
-
C.
isVictimOf
Indicates that one entity suffers harm, loss, or wrongdoing as a result of another entity’s actions or events.
-
D.
hasVictims
Indicates that an entity has one or more individuals who have been harmed, injured, or adversely affected by it.
-
E.
hasVictimPattern
Indicates a recurring or characteristic pattern in the selection, treatment, or circumstances of victims associated with an action or event.
- 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_69f34905e098819082191a6922a6d607 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fcd867f36081908c88c55a6a1404c1 |
completed | May 7, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f47b188190b4cf4b4c748d9d03 |
completed | May 7, 2026, 5:55 p.m. |
| PDg | Predicate description generation | batch_69fcd866dd248190bff61c43bee93f54 |
completed | May 7, 2026, 6:22 p.m. |
Created at: May 1, 2026, 12:32 a.m.