Triple
T2867770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assistant Secretaries of the Air Force |
E63480
|
entity |
| Predicate | minimumNumberSpecifiedInLaw |
P26954
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Assistant Secretaries of the Air Force, minimumNumberSpecifiedInLaw, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: minimumNumberSpecifiedInLaw Context triple: [Assistant Secretaries of the Air Force, minimumNumberSpecifiedInLaw, 4]
-
A.
establishedMandatoryMinimum
Indicates that an authority has set a required minimum level, amount, or standard that must be met or exceeded.
-
B.
minimumNumberOfJustices
Indicates the smallest number of justices required for a court or judicial body to validly conduct its proceedings or make decisions.
-
C.
minimumWidth
Indicates that there is a specified smallest allowable or required width for something in the relationship.
-
D.
minimumNumber
chosen
Indicates that the associated value is the smallest or least quantity allowed, required, or observed within a given set or context.
-
E.
numberOfLaws
Indicates the quantitative count of laws associated with a given entity or context.
- 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_69ab4c42fb8c8190b36e161d47c03b81 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdfbcebcc81909a78a1787d823e3e |
completed | March 7, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69abdd123ec48190af50a1859aea50b7 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.