Triple

T22823597
Position Surface form Disambiguated ID Type / Status
Subject UNLP E565291 entity
Predicate locatedIn P40 FINISHED
Object La Plata E86152 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: La Plata | Statement: [UNLP, locatedIn, La Plata]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: La Plata
Context triple: [UNLP, locatedIn, La Plata]
  • A. La Plata chosen
    La Plata is the planned capital city of Argentina’s Buenos Aires Province, known for its distinctive diagonal street grid and cultural and educational institutions.
  • B. La Plata
    La Plata is a municipality and town in Colombia known for its location in the western part of the Huila Department and its role as a regional agricultural and commercial center.
  • C. La Plata
    La Plata, historically known as the city of Sucre in present-day Bolivia, is a colonial-era Andean city that served as an important administrative and judicial center of the Spanish Empire in South America.
  • D. La Plata
    La Plata is a metro station on the Seville Metro system in Seville, Spain.
  • E. Mar del Plata
    Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
  • 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_69e2458426188190b58b8ab4844fe420 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17dd2005081909baef070124eb839 completed April 29, 2026, 3:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0bc2337900819098b989c47219bc1d completed May 19, 2026, 1:51 a.m.
Created at: April 17, 2026, 3:34 p.m.