Triple
T29720530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shooting of Amadou Diallo |
E752044
|
entity |
| Predicate | numberOfOfficersInvolved |
P201680
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Shooting of Amadou Diallo, numberOfOfficersInvolved, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfOfficersInvolved Context triple: [Shooting of Amadou Diallo, numberOfOfficersInvolved, 4]
-
A.
lawEnforcementInvolved
Indicates that law enforcement authorities are actively involved in or responding to the situation or event described.
-
B.
officersCalled
Indicates that law enforcement officers were summoned or notified to respond to a situation or incident.
-
C.
numberOfInmatesInvolved
Indicates the count of inmates who participated in or were involved in a specified incident or event.
-
D.
involvedOfficer
Indicates that an officer participated in, was connected to, or played a role in a particular incident, case, or event.
-
E.
numberOfFirefightersInvolved
Indicates the total count of firefighters who participated in or were involved in a specific event, incident, or operation.
- 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_69f0d628c00c8190ab5ee7e423d7ec3c |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_6a00144238708190acbec3f791cc873e |
completed | May 10, 2026, 5:14 a.m. |
| PD | Predicate disambiguation | batch_6a00120244a4819090ef39070aba9d99 |
completed | May 10, 2026, 5:05 a.m. |
| PDg | Predicate description generation | batch_6a001440a26c81908ba50779bb6e1679 |
completed | May 10, 2026, 5:14 a.m. |
Created at: April 28, 2026, 7:36 p.m.