Triple
T35790164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenneth Arnold |
E1034671
|
entity |
| Predicate | numberOfObjectsReported |
P48434
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Kenneth Arnold, numberOfObjectsReported, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfObjectsReported Context triple: [Kenneth Arnold, numberOfObjectsReported, 9]
-
A.
numberOfDetectedObjects
Indicates the count of distinct objects that have been identified or recognized in a given context or observation.
-
B.
hasNumberOfObjects
chosen
Indicates that an entity is associated with a specific count or quantity of objects.
-
C.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
-
D.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
E.
numberOfEntities
Indicates the total count of distinct entities involved in or associated with a given context or situation.
- 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_69f76e1575908190aaa306d843b41c14 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:06 p.m.