Triple
T23049827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carmen de la Legua-Reynoso |
E573976
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Callao |
—
|
NE NERFINISHED |
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: Callao | Statement: [Carmen de la Legua-Reynoso, borderedBy, Callao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Callao Context triple: [Carmen de la Legua-Reynoso, borderedBy, Callao]
-
A.
Callao
chosen
Callao is Peru’s chief seaport and a major coastal city adjacent to Lima, serving as the country’s principal gateway for maritime trade.
-
B.
Callao
Callao is a central Madrid Metro station located in the busy commercial and entertainment hub around Plaza del Callao in the city center.
-
C.
Callao
Callao is a small unincorporated community in Northumberland County, Virginia, known primarily as a rural locality in the Northern Neck region of the state.
-
D.
Lima
Lima is a station on Buenos Aires’ historic Underground Line A, serving passengers in the city’s central area.
-
E.
Lima
Lima is a subregion of Portugal’s Vinho Verde wine area, known for producing fresh, aromatic white wines from local grape varieties.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b9c11481909d06c872214d21af |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1867b800881909fabf9dca994c9e7 |
completed | April 29, 2026, 4:18 a.m. |
Created at: April 17, 2026, 3:54 p.m.