Triple

T16014743
Position Surface form Disambiguated ID Type / Status
Subject Indooroopilly E388435 entity
Predicate adjacentSuburb P37779 FINISHED
Object Taringa E1122632 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: Taringa | Statement: [Indooroopilly, adjacentSuburb, Taringa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taringa
Context triple: [Indooroopilly, adjacentSuburb, Taringa]
  • A. Taringa chosen
    Taringa is a residential suburb in Brisbane, Queensland, known for its proximity to the city, strong public transport links, and a mix of students and professionals.
  • B. Tago
    Tago is a coastal municipality in the province of Surigao del Sur in the Philippines, known for its agricultural lands and riverine landscapes.
  • C. Tianguá
    Tianguá is a municipality in northeastern Brazil known for its location in the highlands of the state of Ceará and its role as a regional commercial and agricultural center.
  • D. Tatengue
    Tatengue is the popular nickname of Club Atlético Unión, a traditional football club from Santa Fe, Argentina.
  • E. Cantagalo
    Cantagalo is a municipality in the mountainous interior of Rio de Janeiro state in southeastern Brazil, known for its rural character and historical coffee production.
  • 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_69d86dabcb7c8190b6a39d6831d2fa1b completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e18293ec1081909248e366967850c0 completed April 17, 2026, 12:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffcf284fa481909b571d1bf107fca4 completed May 10, 2026, 12:19 a.m.
Created at: April 10, 2026, 4:55 a.m.