Triple
T10294430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portoviejo |
E241445
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Jipijapa |
E329466
|
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: Jipijapa | Statement: [Portoviejo, nearbyCity, Jipijapa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jipijapa Context triple: [Portoviejo, nearbyCity, Jipijapa]
-
A.
Jipijapa
chosen
Jipijapa is a city in coastal Ecuador known historically for its production of Panama hats and its agricultural economy.
-
B.
Piripiri
Piripiri is a municipality in the Brazilian state of Piauí, known for its regional commerce and proximity to natural attractions such as the Sete Cidades National Park.
-
C.
Zapota
Zapota is a metro station on Mexico City’s Line 12, serving passengers in the southeastern part of the city.
-
D.
Papingo
Papingo is a picturesque traditional village in the Zagori region of Epirus, northwestern Greece, known for its stone architecture and dramatic mountain scenery.
-
E.
Guagua
Guagua is a municipality in the province of Pampanga in the Philippines, known historically as a riverside trading town.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2d5e0f88190be3e23ba2511a1e9 |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71d1c180481909ca9983e14cbb931 |
completed | April 9, 2026, 3:29 a.m. |
Created at: April 6, 2026, 11:42 a.m.