Triple
T1472511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ambassador Hotel, Los Angeles |
E27166
|
entity |
| Predicate | eventOccurred |
P22468
|
FINISHED |
| Object | assassination of Robert F. Kennedy |
—
|
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: assassination of Robert F. Kennedy | Statement: [Ambassador Hotel, Los Angeles, eventOccurred, assassination of Robert F. Kennedy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eventOccurred Context triple: [Ambassador Hotel, Los Angeles, eventOccurred, assassination of Robert F. Kennedy]
-
A.
eventIn
Indicates that an event occurs within, or is situated in, a specific location, context, or larger event.
-
B.
event
Indicates that there exists an occurrence or happening involving one or more entities, typically situated in time and possibly space.
-
C.
typicalEvent
Indicates that the associated event is a common, characteristic, or prototypical occurrence for the given entity or situation.
-
D.
eventUse
Indicates that an event involves the use or utilization of a particular entity (e.g., a resource, tool, or method) as part of its occurrence.
-
E.
hadEvent
chosen
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_69a496d25d6881909dbd84f86d763992 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c5dc90e481908a4935f266bc7850 |
completed | March 1, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69a4c48350d88190a81bd149103f93e3 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8:01 p.m.