Triple
T27635272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | killing of Philip Barton Key II |
E696452
|
entity |
| Predicate | hasVictimName |
P69836
|
FINISHED |
| Object | Philip Barton Key II |
—
|
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: Philip Barton Key II | Statement: [killing of Philip Barton Key II, hasVictimName, Philip Barton Key II]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVictimName Context triple: [killing of Philip Barton Key II, hasVictimName, Philip Barton Key II]
-
A.
hasMainVictim
Indicates that an event, action, or harmful situation primarily targets or affects a specific victim as its main subject.
-
B.
isVictimOf
Indicates that one entity suffers harm, loss, or wrongdoing as a result of another entity’s actions or events.
-
C.
hasVictimCount
Indicates the number of victims associated with a particular event, action, or entity.
-
D.
hasVictims
Indicates that an entity has one or more individuals who have been harmed, injured, or adversely affected by it.
-
E.
victimFullName
chosen
Indicates the complete personal name of the individual who is the victim in the described event or relationship.
- 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_69ef5909f3848190805f35b76833e722 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f6a28c7c148190bfc980aad9f678ca |
completed | May 3, 2026, 1:19 a.m. |
| PD | Predicate disambiguation | batch_69f69fe1e3c88190830bb2e9f407357e |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 27, 2026, 2:23 p.m.