Triple
T17327272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | California oil fields |
E420718
|
entity |
| Predicate | majorEconomicImpactOn |
P42250
|
FINISHED |
| Object | California economy |
—
|
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: California economy | Statement: [California oil fields, majorEconomicImpactOn, California economy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorEconomicImpactOn Context triple: [California oil fields, majorEconomicImpactOn, California economy]
-
A.
stanceOnEconomy
Indicates a subject's expressed position, opinion, or policy view regarding economic issues or economic policy.
-
B.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
C.
majorImpact
chosen
Indicates that one entity has a significant, highly influential, or transformative effect on another entity or outcome.
-
D.
impactOnBusiness
Indicates the effect or influence that one factor, event, or action has on a business’s performance, operations, or outcomes.
-
E.
impactOnIndustry
Indicates the effect or influence that one entity, event, or action has on the state, performance, or development of an industry.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e439d321c881909e63a6870b152c8c |
completed | April 19, 2026, 2:11 a.m. |
| PD | Predicate disambiguation | batch_69e3b021a5bc81909ae55406f9d0b37f |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:43 a.m.