Triple
T13349900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1947 Kenneth Arnold sighting |
E318042
|
entity |
| Predicate | hasWitnessCount |
P58125
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [1947 Kenneth Arnold sighting, hasWitnessCount, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWitnessCount Context triple: [1947 Kenneth Arnold sighting, hasWitnessCount, 1]
-
A.
hasWitnessType
Indicates that an event, incident, or situation is associated with a specific category or type of witness involved.
-
B.
hasProofCount
Indicates the number of proofs or supporting evidential items associated with a given entity or claim.
-
C.
numberOfWitnessesHeard
chosen
Indicates the count of witnesses whose testimony or statements were heard in a given event or proceeding.
-
D.
hasQuorum
Indicates that the number of required participants or members is sufficient to validly conduct the specified activity or decision.
-
E.
hasNumberOfVotingMembers
Indicates the specific count of individuals who hold voting rights within a given group or body.
- 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.