Triple
T13721830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Spain |
E329055
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Jaén |
E66602
|
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: Jaén | Statement: [Southern Spain, hasCity, Jaén]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jaén Context triple: [Southern Spain, hasCity, Jaén]
-
A.
Jaén
chosen
Jaén is a province in southern Spain’s Andalusia region, renowned for its vast olive groves and historic Renaissance towns.
-
B.
Jaén
Jaén is a significant commercial and agricultural city in northern Peru, known as a regional hub within the Cajamarca Region.
-
C.
Jaen
Jaen is a landlocked agricultural municipality in the province of Nueva Ecija in the Central Luzon region of the Philippines.
-
D.
Jerez de la Frontera
Jerez de la Frontera is a historic city in southwestern Spain renowned for its sherry wine production, flamenco heritage, and equestrian traditions.
-
E.
Écija
Écija is a historic Andalusian city in southern Spain, renowned for its baroque architecture and extremely hot summer climate that has earned it the nickname "the frying pan of Andalusia."
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de01f3b46481909ceedfa78e9ca92b |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd54f748d481909661deb151da34c1 |
completed | May 8, 2026, 3:13 a.m. |
Created at: April 9, 2026, 9:55 p.m.