Triple
T2759836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palpa |
E61192
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Ica |
E284236
|
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: Ica | Statement: [Palpa, near, Ica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ica Context triple: [Palpa, near, Ica]
-
A.
Ica
chosen
Ica is a city in southern Peru known for its desert landscape, nearby Huacachina oasis, and production of pisco and wine.
-
B.
Sangolquí
Sangolquí is a city in central Ecuador known as a growing suburban and commercial center near the capital, Quito, within Pichincha Province.
-
C.
Girón
Girón is a historic colonial-era town and municipality in northeastern Colombia, renowned for its preserved whitewashed architecture and cobblestone streets.
-
D.
Supía
Supía is a municipality in the Caldas Department of Colombia, known historically for gold mining and its indigenous Emberá Chamí heritage.
-
E.
Balvanera
Balvanera is a densely populated, traditionally working- and middle-class neighborhood in central Buenos Aires, Argentina, known for its historic architecture, commercial activity, and strong cultural life.
- 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_69ab4b7bab6c8190a5c2efef19a8ef34 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd2121548190b96f174e6f61f9b5 |
completed | March 7, 2026, 8:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbe2d9cc81909b041635ae793bc3 |
completed | March 10, 2026, 6:36 a.m. |
Created at: March 6, 2026, 9:57 p.m.