Triple
T15567603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ourém |
E374155
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Alcanena |
E378288
|
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: Alcanena | Statement: [Ourém, locatedNear, Alcanena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alcanena Context triple: [Ourém, locatedNear, Alcanena]
-
A.
Alcanena
chosen
Alcanena is a Portuguese municipality known for its traditional leather and tanning industry, located in the Centro Region of Portugal.
-
B.
Arévalo
Arévalo is a historic town in Spain’s Castile and León region, known for its well-preserved medieval architecture and Mudejar-style monuments.
-
C.
Mencía
"Mencía" is a song featured on the album *American Dream*, likely reflecting its themes of aspiration and personal struggle.
-
D.
La Bañeza
La Bañeza is a small historic city in northwestern Spain known for its cultural festivals and traditional architecture.
-
E.
Almendralejo
Almendralejo is a town in the Spanish region of Extremadura known for its wine production and agricultural economy.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04dde90b081908284d9258d4462e3 |
completed | April 16, 2026, 2:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82e56ffc81909e3228a660df4e09 |
completed | May 9, 2026, 6:54 p.m. |
Created at: April 10, 2026, 4:10 a.m.