Triple
T2209022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Furious Fifties |
E50869
|
entity |
| Predicate | hasCharacteristicWeather |
P2044
|
FINISHED |
| Object | stormy conditions |
—
|
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: stormy conditions | Statement: [Furious Fifties, hasCharacteristicWeather, stormy conditions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCharacteristicWeather Context triple: [Furious Fifties, hasCharacteristicWeather, stormy conditions]
-
A.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
B.
hasWeather
chosen
Indicates that a location or environment is experiencing or characterized by a particular type of weather condition.
-
C.
hasClimate
Indicates that an entity possesses or is characterized by a particular type of climate or climatic conditions.
-
D.
hasClimateContext
Indicates that something is associated with, influenced by, or relevant to climate-related conditions, factors, or considerations.
-
E.
winterCharacteristic
Indicates a characteristic, feature, or quality that is specifically associated with or typical of winter.
- 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_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. |
Created at: March 4, 2026, 7:46 p.m.