Triple
T3650003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Hollywood shootout |
E77395
|
entity |
| Predicate | casualtiesPoliceInjured |
P50024
|
FINISHED |
| Object | 12 |
—
|
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: 12 | Statement: [North Hollywood shootout, casualtiesPoliceInjured, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesPoliceInjured Context triple: [North Hollywood shootout, casualtiesPoliceInjured, 12]
-
A.
casualtiesCiviliansWounded
Indicates that an event or action resulted in civilian individuals being wounded or injured.
-
B.
involvedInAccident
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
-
C.
policeNumber
Indicates that an entity is associated with a specific police-related identification or reference number.
-
D.
officersCalled
Indicates that law enforcement officers were summoned or notified to respond to a situation or incident.
-
E.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
- 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_69ad85def5cc8190863dccf55a18bebb |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc38fa1988190b630329700afc3dd |
completed | March 8, 2026, 6:44 p.m. |
| PD | Predicate disambiguation | batch_69adb8445b2c8190ab07f6ad4e010d0e |
completed | March 8, 2026, 5:56 p.m. |
| PDg | Predicate description generation | batch_69adb8e4ba948190a9b777cf7f788b96 |
completed | March 8, 2026, 5:59 p.m. |
Created at: March 8, 2026, 3:24 p.m.