Triple
T21758687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surquillo District |
E537104
|
entity |
| Predicate | locatedInCountryCapitalArea |
P5025
|
FINISHED |
| Object | Lima |
E2605
|
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: Lima | Statement: [Surquillo District, locatedInCountryCapitalArea, Lima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lima Context triple: [Surquillo District, locatedInCountryCapitalArea, Lima]
-
A.
Lima
Lima is a station on Buenos Aires’ historic Underground Line A, serving passengers in the city’s central area.
-
B.
Lima
chosen
Lima is the capital and largest city of Peru, known as a major political, economic, and cultural center on South America's Pacific coast.
-
C.
Lima
Lima is a subregion of Portugal’s Vinho Verde wine area, known for producing fresh, aromatic white wines from local grape varieties.
-
D.
Sucre
Sucre is a coastal state in northeastern Venezuela known for its Caribbean shoreline, fishing communities, and colonial-era towns.
-
E.
Sucre
Sucre is a neighborhood or locality within the Chapinero district of Bogotá, Colombia, known primarily as a residential and commercial urban area.
- 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01d902c9881908051904e44a136af |
completed | April 28, 2026, 2:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a45d2d6c8819084f47207366689c9 |
completed | May 17, 2026, 10:48 p.m. |
Created at: April 16, 2026, 6:50 p.m.