Triple
T23510404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Henry Ayers |
E572403
|
entity |
| Predicate | numberOfSeparateTermsAsPremier |
P9908
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Henry Ayers, numberOfSeparateTermsAsPremier, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSeparateTermsAsPremier Context triple: [Henry Ayers, numberOfSeparateTermsAsPremier, 7]
-
A.
premierTermDependentOn
Indicates that the validity, duration, or conditions of a premier term are contingent upon another specified factor, agreement, or event.
-
B.
hasNumberOfTerms
chosen
Indicates the quantity of distinct terms or elements associated with a given entity or expression.
-
C.
firstTerms
Indicates that the related entities are the initial elements or starting terms in a sequence, series, or ordered collection.
-
D.
numberOfTermsInHouse
Indicates the total count of terms an individual has served in a legislative house.
-
E.
premierNumber
Indicates that the entity is identified as the first or leading item in an ordered sequence, such as a top-ranked or primary position.
- 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_69e245b5e4208190bac8a6509867e394 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a90455f0819092b37c69d7e73c43 |
completed | April 29, 2026, 6:45 a.m. |
| PD | Predicate disambiguation | batch_69f0621165c08190a0b27b1319733959 |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 6:07 p.m.