Triple
T5703877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kent State shootings |
E125735
|
entity |
| Predicate | numberOfPeopleWounded |
P63693
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Kent State shootings, numberOfPeopleWounded, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeopleWounded Context triple: [Kent State shootings, numberOfPeopleWounded, 9]
-
A.
numberOfVictimsInjured
chosen
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
B.
casualtiesCiviliansWounded
Indicates that an event or action resulted in civilian individuals being wounded or injured.
-
C.
numberOfPeopleLaterDyingOfInjuriesConsidered
Indicates the number of people who subsequently died from injuries that were previously evaluated or taken into account.
-
D.
wasWoundedIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or conflict.
-
E.
casualtiesWoundedUS
Indicates that the relationship specifies the number of U.S. individuals who were wounded as casualties in an 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_69c0082c96988190b3a6a201edce472a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c024585d14819098ec34fd5a858836 |
completed | March 22, 2026, 5:18 p.m. |
| PD | Predicate disambiguation | batch_69c021c2d8bc8190b947c7d1f423d2f3 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:45 p.m.