Triple

T13721838
Position Surface form Disambiguated ID Type / Status
Subject Southern Spain E329055 entity
Predicate hasCity P316 FINISHED
Object Baeza E414300 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: Baeza | Statement: [Southern Spain, hasCity, Baeza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Baeza
Context triple: [Southern Spain, hasCity, Baeza]
  • A. Baeza chosen
    Baeza is a historic Andalusian town in southern Spain renowned for its well-preserved Renaissance architecture and status as a UNESCO World Heritage Site.
  • B. Alhué
    Alhué is a rural commune and town in central Chile known for its agricultural activities and traditional countryside character within the Santiago Metropolitan Region.
  • C. 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.
  • D. Ayamonte
    Ayamonte is a Spanish border town in the province of Huelva, Andalusia, situated at the mouth of the Guadiana River opposite Portugal.
  • E. Béjar
    Béjar is a historic town in the province of Salamanca, Spain, known for its textile heritage and scenic setting in the Sierra de Béjar mountains.
  • 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_69fd6471ba348190a0cd1745daff6114 completed May 8, 2026, 4:20 a.m.
Created at: April 9, 2026, 9:55 p.m.