Triple
T19928698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | מְפִיבֹשֶׁת |
E478994
|
entity |
| Predicate | accidentOccurredDuring |
P137871
|
FINISHED |
| Object | flight after news of Saul and Jonathan’s deaths |
—
|
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: flight after news of Saul and Jonathan’s deaths | Statement: [מְפִיבֹשֶׁת, accidentOccurredDuring, flight after news of Saul and Jonathan’s deaths]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: accidentOccurredDuring Context triple: [מְפִיבֹשֶׁת, accidentOccurredDuring, flight after news of Saul and Jonathan’s deaths]
-
A.
hasAccidentAt
Indicates that an accident involving a subject occurs at a specific location or time.
-
B.
injuryOccurredAt
Indicates that an injury took place at a specific location or during a particular event or time.
-
C.
involvedInAccident
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
-
D.
causedAccident
Indicates that one entity is responsible for bringing about or initiating an accident involving another entity or situation.
-
E.
accident
Indicates an unintended, unforeseen event or mishap occurring, often resulting in damage, injury, or disruption.
- 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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e659cd4500819090363a4b7d6bf193 |
completed | April 20, 2026, 4:52 p.m. |
| PD | Predicate disambiguation | batch_69e537f070b481908958e0e5911dcdc1 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c136b081909cab9394b958390a |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:53 p.m.