Triple
T30579797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Macko |
E778351
|
entity |
| Predicate | numberOfPeopleKilledInSameEvent |
P122935
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [William Macko, numberOfPeopleKilledInSameEvent, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeopleKilledInSameEvent Context triple: [William Macko, numberOfPeopleKilledInSameEvent, 6]
-
A.
numberOfVictimsInSameEvent
Indicates the count of distinct victims involved in the same specific event or incident.
-
B.
numberOfVictimsKilled
Indicates the count of victims who were killed as a result of the referenced event or action.
-
C.
numberOfPeopleReportedKilled
chosen
Indicates the reported count of people who have been killed in an incident or event.
-
D.
numberOfVictimsConfirmed
Indicates the confirmed count of victims associated with an event, incident, or situation.
-
E.
numberOfPeopleKilledOnGround
Indicates the count of people who were killed on the ground as a result of the event or incident.
- 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_69f224a04b248190b0ca443ec86207b8 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a00818b20a881909fbf3bb33dcf7029 |
completed | May 10, 2026, 1 p.m. |
| PD | Predicate disambiguation | batch_6a0080f76f588190a933238861243d1a |
completed | May 10, 2026, 12:58 p.m. |
Created at: April 29, 2026, 8:23 p.m.