Triple
T17843759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montilla |
E445600
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Lucena |
—
|
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: Lucena | Statement: [Montilla, locatedNear, Lucena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lucena Context triple: [Montilla, locatedNear, Lucena]
-
A.
Lucena
Lucena is a coastal city in the Philippines that serves as the capital and commercial hub of Quezon Province in the Southern Tagalog region.
-
B.
Lucena
chosen
Lucena is a historic city in the province of Córdoba, Andalusia, southern Spain, known for its rich cultural heritage and former Jewish community.
-
C.
Lucena
Lucena is a coastal municipality in the state of Paraíba, Brazil, known for its beaches and proximity to the capital city João Pessoa.
-
D.
Aranjuez
Aranjuez is a historic town in central Spain renowned for its royal palace, extensive gardens, and cultural heritage, and is located within the Community of Madrid.
-
E.
Badajoz
Badajoz is a historic city in western Spain near the Portuguese border, known for its medieval fortress and role as a strategic frontier stronghold.
- 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_69d8b9f1a6d881909f024bc603111cdb |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48ff8730881908cc8e1b572fa0af8 |
completed | April 19, 2026, 8:19 a.m. |
Created at: April 10, 2026, 10:16 a.m.