Triple
T8308404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sucre, Colombia |
E194522
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Sincelejo |
E567451
|
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: Sincelejo | Statement: [Sucre, Colombia, hasMunicipality, Sincelejo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sincelejo Context triple: [Sucre, Colombia, hasMunicipality, Sincelejo]
-
A.
Sincelejo
chosen
Sincelejo is a city in northern Colombia that serves as the political and economic center of the Sucre Department.
-
B.
Mazunte
Mazunte is a small, laid-back beach town on Mexico’s Oaxacan coast, known for its sea turtle conservation center, eco-tourism, and scenic Pacific shoreline.
-
C.
Jiguaní
Jiguaní is a municipality and town in eastern Cuba known for its historical role in the country’s wars of independence.
-
D.
Ojojona
Ojojona is a historic town in southern Honduras known for its colonial architecture and traditional crafts.
-
E.
Sangolquí
Sangolquí is a city in central Ecuador known as a growing suburban and commercial center near the capital, Quito, within Pichincha Province.
- 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_69ca82e613e88190bf8139669bbd0d53 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f2c06608190bd21633af07a530b |
completed | March 31, 2026, 8 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc6e4eb808190b138c52810f35040 |
completed | April 2, 2026, 1:31 a.m. |
Created at: March 30, 2026, 5:54 p.m.