Triple
T2286054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Mexico |
E51394
|
entity |
| Predicate | hasSubregion |
P285
|
FINISHED |
| Object | Tabasco |
E57708
|
NE 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: Tabasco | Statement: [Southern Mexico, hasSubregion, Tabasco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tabasco Context triple: [Southern Mexico, hasSubregion, Tabasco]
-
A.
Tabasco
chosen
Tabasco is a southeastern Mexican state along the Gulf of Mexico, known for its tropical climate, petroleum industry, and rich wetlands.
-
B.
Cholula
Cholula is a historic Mexican city famed for its Great Pyramid and rich pre-Hispanic and colonial heritage.
-
C.
Cayenne
Cayenne is the principal city and administrative center of French Guiana, located on the Atlantic coast in northeastern South America.
-
D.
Manteca
Manteca is a city in California’s Central Valley known for its agricultural roots, suburban growth, and role as a commuter hub between the Bay Area and inland communities.
-
E.
Canela
Canela is a coastal rural municipality in Chile’s Coquimbo Region, known for its small agricultural communities and semi-arid landscapes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a88b09c644819090b503456d96bf70 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc24730208190af8a5cf443d334f7 |
completed | March 7, 2026, 6:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae7f1759b081908842f7ad189994ff |
completed | March 9, 2026, 8:04 a.m. |
Created at: March 4, 2026, 7:48 p.m.