Triple

T15803634
Position Surface form Disambiguated ID Type / Status
Subject Cantar de mio Cid E383157 entity
Predicate setting P1957 FINISHED
Object Valencia E13494 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: Valencia | Statement: [Cantar de mio Cid, setting, Valencia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valencia
Context triple: [Cantar de mio Cid, setting, Valencia]
  • A. Valencia chosen
    Valencia is a major Spanish coastal city known for its historic architecture, vibrant culture, and significant role as a key Mediterranean trade and tourism hub.
  • B. Valencia
    Valencia is a municipality in the Philippine province of Negros Oriental known for its cool climate, geothermal energy resources, and natural attractions such as waterfalls and mountain landscapes.
  • C. Valencia
    Valencia is a city in Ecuador that serves as the capital of Los Ríos Province’s Valencia Canton and is known for its agricultural surroundings and tropical climate.
  • D. Valencia
    Valencia is a major inland city in the Philippine province of Bukidnon, known as a commercial and agricultural hub in Northern Mindanao.
  • E. Valencia
    Valencia is a city located in the highland province of Bukidnon in the Philippines, known as a major agricultural and commercial center in the region.
  • 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_69d86da2858c819090cc8481e7207b6e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e0b524835c8190ae286b2562f07756 completed April 16, 2026, 10:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffa124e7d48190ac25e9541dea0122 completed May 9, 2026, 9:03 p.m.
Created at: April 10, 2026, 4:48 a.m.