Triple
T1459484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villeta |
E31476
|
entity |
| Predicate | hasRegionalImportanceFor |
P12510
|
FINISHED |
| Object | sugarcane industry |
—
|
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: sugarcane industry | Statement: [Villeta, hasRegionalImportanceFor, sugarcane industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRegionalImportanceFor Context triple: [Villeta, hasRegionalImportanceFor, sugarcane industry]
-
A.
hasRegionalSignificance
chosen
Indicates that something holds particular importance, influence, or relevance within a specific geographic region.
-
B.
hasNationalImportance
Indicates that something possesses significance or relevance at the level of an entire nation, rather than just local or regional importance.
-
C.
regionallyAssociatedWith
Indicates that two entities are connected or related based on sharing the same or overlapping geographic or regional context.
-
D.
impactRegion
Indicates the geographic or spatial area that is affected or influenced by a particular event, action, or phenomenon.
-
E.
geopoliticalSignificance
Indicates the importance or influence that a place, event, or relationship holds within regional or global political and strategic power dynamics.
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c59c1c288190be08064f2d351b2b |
completed | March 1, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69a4c47ec5108190b1772237f2e5d90b |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.