Triple

T8181337
Position Surface form Disambiguated ID Type / Status
Subject SUBE card E191067 entity
Predicate usedIn P98 FINISHED
Object Mar del Plata E304269 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: Mar del Plata | Statement: [SUBE card, usedIn, Mar del Plata]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mar del Plata
Context triple: [SUBE card, usedIn, Mar del Plata]
  • A. Mar del Plata chosen
    Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
  • B. 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.
  • C. Buenos Aires
    Buenos Aires is the capital and largest city of Argentina, known for its rich European-influenced culture, tango music and dance, and vibrant urban life.
  • D. 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.
  • E. 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.
  • 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_69ca82c4538081909404325aa5639483 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4c4c2e388190b86854f8b1765e61 completed March 31, 2026, 4:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69d02f2a6ca88190b3f234447feab6e3 completed April 3, 2026, 9:20 p.m.
Created at: March 30, 2026, 5:40 p.m.