Triple
T10698854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loja Province |
E252216
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Loja |
E241447
|
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: Loja | Statement: [Loja Province, capital, Loja]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Loja Context triple: [Loja Province, capital, Loja]
-
A.
Loja
chosen
Loja is a city in southern Ecuador known as a cultural and musical center nestled in the Andean highlands.
-
B.
Loja
Loja is a historic town in the province of Granada, Spain, known for its strategic location between Granada and the coast and its role in the final stages of the Reconquista.
-
C.
Feira Nova
Feira Nova is a small municipality located in the semi-arid interior region of the state of Sergipe, Brazil.
-
D.
Neiva
Neiva is a major city in southwestern Colombia known as the economic and cultural center of the upper Magdalena River valley.
-
E.
Lajeado
Lajeado is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd8a03848190bf68dd470bee103a |
completed | April 9, 2026, 1:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d998ed78e481908537ae10d55e6f65 |
completed | April 11, 2026, 12:42 a.m. |
Created at: April 8, 2026, 9:12 p.m.