Triple

T6495822
Position Surface form Disambiguated ID Type / Status
Subject Entre Ríos Province E148156 entity
Predicate containsCity P294 FINISHED
Object Gualeguaychú E181906 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: Gualeguaychú | Statement: [Entre Ríos Province, containsCity, Gualeguaychú]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gualeguaychú
Context triple: [Entre Ríos Province, containsCity, Gualeguaychú]
  • A. Gualeguaychú chosen
    Gualeguaychú is a city in eastern Argentina known for its vibrant Carnival celebrations and riverside tourism.
  • B. Tandil
    Tandil is a mid-sized city in central Argentina known for its scenic hilly landscapes, stone formations, and tourism-focused outdoor activities.
  • C. Río Cuarto
    Río Cuarto is a major city in central Argentina known as an important commercial, agricultural, and educational hub within Córdoba Province.
  • D. Catanduva
    Catanduva is a municipality in the northwestern region of the state of São Paulo, Brazil, known for its agricultural production and regional commercial importance.
  • E. Bahía Blanca
    Bahía Blanca is a major port city in southern Buenos Aires Province, Argentina, known for its industrial activity and strategic location on the Atlantic coast.
  • 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_69c009088f3081909cd467b05919de30 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c06ab958808190bd85e007e925ffc4 completed March 22, 2026, 10:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c72f6db074819089795c29cb4f6699 completed March 28, 2026, 1:31 a.m.
Created at: March 22, 2026, 4:53 p.m.