Triple

T15298822
Position Surface form Disambiguated ID Type / Status
Subject International Table Tennis Federation E365731 entity
Predicate headquartersLocation P62 FINISHED
Object Lausanne E74605 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: Lausanne | Statement: [International Table Tennis Federation, headquartersLocation, Lausanne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lausanne
Context triple: [International Table Tennis Federation, headquartersLocation, Lausanne]
  • A. Lausanne chosen
    Lausanne is a major Swiss city on the shores of Lake Geneva, known for hosting the International Olympic Committee and its vibrant cultural and academic institutions.
  • B. Neuchâtel
    Neuchâtel is a French-speaking canton in western Switzerland known for its lakeside capital, watchmaking industry, and historic architecture.
  • C. Geneva
    Geneva is a major Swiss city on Lake Geneva known for hosting numerous international organizations, including United Nations agencies and the Red Cross.
  • D. Geneva
    Geneva is a small city in northeastern Ohio situated along Lake Erie, known for its wineries, tourism, and location within the Rust Belt region.
  • E. Geneva
    Geneva is a small city in New York's Finger Lakes region, known for its lakeside setting on Seneca Lake and its historic colleges and wineries.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03686bfb8819080ba0caae652170a completed April 16, 2026, 1:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff21945b60819098ea91d9693cb8e3 completed May 9, 2026, 11:59 a.m.
Created at: April 10, 2026, 3:15 a.m.