Triple
T2020599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas–New York |
E44094
|
entity |
| Predicate | oftenContrasts |
P11289
|
FINISHED |
| Object | Sun Belt state |
—
|
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: Sun Belt state | Statement: [Texas–New York, oftenContrasts, Sun Belt state]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenContrasts Context triple: [Texas–New York, oftenContrasts, Sun Belt state]
-
A.
oftenContrastedWith
chosen
Indicates that one entity is frequently compared to another in a way that highlights their differences or opposing characteristics.
-
B.
exploresContrastBetween
Indicates a relationship in which one entity examines, highlights, or analyzes the differences or oppositions between two or more entities, ideas, or situations.
-
C.
oftenConfusedWith
Indicates that one entity is frequently mistaken for or thought to be another due to similarity or ambiguity.
-
D.
genreContrast
Indicates a relationship where two or more works are compared or juxtaposed based on differences between their genres.
-
E.
hasConceptualOpposite
Indicates that one entity represents a concept that is fundamentally opposed or contrary in meaning to the concept represented by another entity.
- 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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8d0bcbc8190bbbb726ecae1c51b |
completed | March 7, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69abb7a389408190a84a54856352f15b |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:38 p.m.