Triple
T13349902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1947 Kenneth Arnold sighting |
E318042
|
entity |
| Predicate | hasWitnessOccupation |
P105570
|
FINISHED |
| Object | businessman |
—
|
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: businessman | Statement: [1947 Kenneth Arnold sighting, hasWitnessOccupation, businessman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWitnessOccupation Context triple: [1947 Kenneth Arnold sighting, hasWitnessOccupation, businessman]
-
A.
hasHumanOccupationEvidence
Indicates that there is supporting evidence that a human has held or currently holds a particular occupation or job role.
-
B.
hasOccupationOfDesignee
Indicates that one entity serves as the designated or appointed holder of an occupation or role for another entity.
-
C.
holderIsOccupation
Indicates that the holder entity has the specified occupation or job role.
-
D.
hasNotableBearerOccupation
Indicates that an entity is associated with a notable person who holds a specific occupation.
-
E.
hasWitnessType
chosen
Indicates that an event, incident, or situation is associated with a specific category or type of witness involved.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99e8c2f1c819094f0970f35f18afa |
completed | April 11, 2026, 1:06 a.m. |
| PD | Predicate disambiguation | batch_69d98f6e53d88190bd6aa42f69b10ffb |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:31 p.m.