Triple
T13076322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nick Curran |
E329584
|
entity |
| Predicate | hasPastIncident |
P37550
|
FINISHED |
| Object | shooting of tourists while on duty |
—
|
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: shooting of tourists while on duty | Statement: [Nick Curran, hasPastIncident, shooting of tourists while on duty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPastIncident Context triple: [Nick Curran, hasPastIncident, shooting of tourists while on duty]
-
A.
hasHistoryOf
chosen
Indicates that an entity has a documented prior occurrence or background of a specified condition, event, or state.
-
B.
hasNotableIncident
Indicates that an entity is associated with a significant or noteworthy event, occurrence, or incident.
-
C.
hasIncidence
Indicates that a particular event, condition, or phenomenon occurs at a certain rate, frequency, or number of cases within a defined population or context.
-
D.
hasAccidentAt
Indicates that an accident involving a subject occurs at a specific location or time.
-
E.
hadEvent
Indicates that an entity experienced, hosted, or was associated with a specific event at some point in time.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98117209081908272021013df2222 |
completed | April 10, 2026, 11 p.m. |
| PD | Predicate disambiguation | batch_69d9803d46688190bac6b7d208f08d01 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:01 p.m.