Triple
T122232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Power Five |
E2472
|
entity |
| Predicate | typicalFeatures |
P5084
|
FINISHED |
| Object | large athletic budgets |
—
|
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: large athletic budgets | Statement: [Power Five, typicalFeatures, large athletic budgets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFeatures Context triple: [Power Five, typicalFeatures, large athletic budgets]
-
A.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
B.
typicalFlavor
Indicates that something characteristically has or is associated with a particular flavor.
-
C.
typicalProductionType
Indicates the usual or characteristic type of production activity associated with an entity.
-
D.
typicalKey
Indicates that the referenced key is the standard or most commonly used key associated with an entity or context.
-
E.
typicalBackground
Indicates that an entity has a usual or commonly expected background, context, or setting associated with it.
- F. None of above. chosen
Provenance (4 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a2573b4e7481909ee09d2899f8a74b |
completed | Feb. 28, 2026, 2:47 a.m. |
| PD | Predicate disambiguation | batch_69a2564928208190966a619680a0d6e2 |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a256c72f6c81909b619b90d829d86e |
completed | Feb. 28, 2026, 2:45 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.