Triple

T4931723
Position Surface form Disambiguated ID Type / Status
Subject Porrentruy E110710 entity
Predicate twinnedWith P1072 FINISHED
Object Saint-Affrique E99838 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: Saint-Affrique | Statement: [Porrentruy, twinnedWith, Saint-Affrique]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saint-Affrique
Context triple: [Porrentruy, twinnedWith, Saint-Affrique]
  • A. Saint-Affrique chosen
    Saint-Affrique is a small historic town in southern France known for its rural charm and location in the Aveyron department.
  • B. Kwaluseni
    Kwaluseni is a town in Eswatini known primarily as the main campus site of the University of Eswatini.
  • C. Vereeniging, South Africa
    Vereeniging is an industrial city in South Africa’s Gauteng province, historically known for its steel and coal industries and its role in the country’s mining and manufacturing economy.
  • D. Lanseria
    Lanseria is a town in the northwestern part of Johannesburg, South Africa, known primarily for hosting the privately owned Lanseria International Airport.
  • E. Soshanguve
    Soshanguve is a large township in the northern part of the Gauteng province of South Africa, known for its diverse population and proximity to Pretoria.
  • 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_69bd4415190c8190817bee7ec9f9f944 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd7063c57c8190a5a6fb3586238d35 completed March 20, 2026, 4:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69be77b18b6c8190bbc8d0764f6a03bf completed March 21, 2026, 10:49 a.m.
Created at: March 20, 2026, 1:30 p.m.