Triple
T2209048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Furious Fifties |
E50869
|
entity |
| Predicate | hasTypicalUseContext |
P37480
|
FINISHED |
| Object | meteorology |
—
|
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: meteorology | Statement: [Furious Fifties, hasTypicalUseContext, meteorology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalUseContext Context triple: [Furious Fifties, hasTypicalUseContext, meteorology]
-
A.
hasTypicalUsageRegion
Indicates that something is most commonly or characteristically used within a particular geographic region.
-
B.
hasUseCase
Indicates that one entity is employed, applied, or utilized as a solution or method to address a particular need, problem, or scenario associated with another entity.
-
C.
canUse
Indicates that one entity has the ability, permission, or suitability to make use of another entity or resource.
-
D.
eligibleUses
Indicates the types of actions, purposes, or contexts in which something is permitted or qualified to be used.
-
E.
usageType
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1baa0948190b07ffc347a4f714e |
completed | March 7, 2026, 6:12 a.m. |
| PD | Predicate disambiguation | batch_69abbda8a6dc8190aa855ce2d17194b1 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abc1b912c08190b9d7bc9230e49d1d |
completed | March 7, 2026, 6:12 a.m. |
Created at: March 4, 2026, 7:46 p.m.