Triple

T9823966
Position Surface form Disambiguated ID Type / Status
Subject Mérignac E238606 entity
Predicate hasTwinTown P919 FINISHED
Object Vila Real E224908 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: Vila Real | Statement: [Mérignac, hasTwinTown, Vila Real]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vila Real
Context triple: [Mérignac, hasTwinTown, Vila Real]
  • A. Vila Real chosen
    Vila Real is a historic city in northern Portugal known for its scenic Douro Valley surroundings and notable Baroque architecture.
  • B. Santo Tirso
    Santo Tirso is a municipality in northern Portugal known for its textile industry, historic monasteries, and location in the Porto metropolitan area.
  • C. Sabugal
    Sabugal is a historic municipality and town in central Portugal, known for its medieval castle and scenic location near the Spanish border.
  • D. Sabrosa
    Sabrosa is a small municipality in Portugal’s Douro region, historically notable as the birthplace of explorer Ferdinand Magellan.
  • E. Vila Real de Santo António
    Vila Real de Santo António is a coastal town and municipality in Portugal’s Algarve region, located at the mouth of the Guadiana River on the border with Spain.
  • 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_69ca84e0dd1881909800765d1e21f735 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb316f8948190ada3738787a5cb6a completed April 2, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1d5b9110881909405d94e0db40eac completed April 5, 2026, 3:23 a.m.
Created at: March 30, 2026, 8:31 p.m.