Triple
T36077973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claude Nobs |
E1043550
|
entity |
| Predicate | helpedDuringEvent |
P149
|
FINISHED |
| Object | rescue of people during the Montreux Casino fire in 1971 |
—
|
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: rescue of people during the Montreux Casino fire in 1971 | Statement: [Claude Nobs, helpedDuringEvent, rescue of people during the Montreux Casino fire in 1971]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: helpedDuringEvent Context triple: [Claude Nobs, helpedDuringEvent, rescue of people during the Montreux Casino fire in 1971]
-
A.
benefitedFromEvent
Indicates that an entity gained an advantage, improvement, or positive outcome as a result of a particular event.
-
B.
helpedCause
Indicates that one entity contributed to bringing about, enabling, or facilitating an outcome or event involving another entity.
-
C.
volunteeredFor
Indicates that an entity willingly offered their time or services to support or participate in an activity, cause, or organization.
-
D.
helpedPerform
Indicates that one entity assisted or contributed to another entity’s performance of an action or task.
-
E.
participatedInEvent
chosen
Indicates that an entity took part in or was actively involved in a specific event.
- 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_69f76e3154908190a6f702671c2bea08 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7ba6d06f48190a71b5a2f19e2232f |
completed | May 3, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a4aad48190a62e41c5e39339d9 |
completed | May 3, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:08 p.m.