Triple

T22741197
Position Surface form Disambiguated ID Type / Status
Subject Mulhouse–Belfort railway E562417 entity
Predicate connects P390 FINISHED
Object Belfort NE NERFINISHED

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: Belfort | Statement: [Mulhouse–Belfort railway, connects, Belfort]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Belfort
Context triple: [Mulhouse–Belfort railway, connects, Belfort]
  • A. Belfort
    Belfort is the surname of Jordan Belfort, the American former stockbroker, motivational speaker, and author whose high-profile fraud case inspired the film "The Wolf of Wall Street."
  • B. Bourg
    Bourg is a metro station on the Lille Metro network in northern France, serving local passengers on Line 2.
  • C. Montélimar
    Montélimar is a town in southeastern France, known as the "gateway to Provence" and famous for its traditional nougat confectionery.
  • D. Dijon
    Dijon is a historic city in eastern France renowned for its rich architectural heritage, former status as the capital of the Duchy of Burgundy, and its famous mustard.
  • E. City of Belfort chosen
    The City of Belfort is a historic commune in northeastern France, known for its strategic fortress, rich military heritage, and the monumental Lion of Belfort sculpture by Frédéric Bartholdi.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245513a5c81908d5cb471b4fc429d completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17972fde8819086094cea289a2af8 completed April 29, 2026, 3:22 a.m.
Created at: April 17, 2026, 3:23 p.m.