Triple

T15329342
Position Surface form Disambiguated ID Type / Status
Subject Entre Ríos E366491 entity
Predicate hasCity P316 FINISHED
Object Villaguay E646621 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: Villaguay | Statement: [Entre Ríos, hasCity, Villaguay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Villaguay
Context triple: [Entre Ríos, hasCity, Villaguay]
  • A. Villaguay chosen
    Villaguay is a city in central Argentina known as an important agricultural and commercial hub within Entre Ríos Province.
  • B. Río Turbio
    Río Turbio is a coal-mining town in southern Patagonia, Argentina, known for its significant lignite deposits and harsh, windy climate.
  • C. Río Rocha
    Río Rocha is a river in central Bolivia that flows through the city of Cochabamba and plays a key role in the region’s drainage and environmental conditions.
  • D. Gualeguay
    Gualeguay is a city in eastern Argentina known for its agricultural economy and location along the Gualeguay River in Entre Ríos Province.
  • E. Río Tunuyán
    Río Tunuyán is a river in western Argentina that flows through Mendoza Province, supporting regional agriculture and viticulture before joining the Desaguadero River system.
  • 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_69d85a121520819093dcce999fdefe1a completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e0161ac8190aa1d52c063c02ad0 completed April 16, 2026, 1:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a002d966ffc8190aa0d9d3abf8ad593 completed May 10, 2026, 7:02 a.m.
Created at: April 10, 2026, 3:16 a.m.