Triple

T10423159
Position Surface form Disambiguated ID Type / Status
Subject Line 7 (Geneva) E245713 entity
Predicate fareSystem P395 FINISHED
Object Unireso E127786 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: Unireso | Statement: [Line 7 (Geneva), fareSystem, Unireso]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Unireso
Context triple: [Line 7 (Geneva), fareSystem, Unireso]
  • A. unireso chosen
    unireso is the integrated public transport fare network for the Geneva region, coordinating tickets and tariffs across multiple operators and modes of transport.
  • B. Osan University
    Osan University is a higher education institution located in Osan, a city in Gyeonggi Province, South Korea.
  • C. Todai
    Todai is the common nickname for the University of Tokyo, Japan’s most prestigious and influential national research university.
  • D. Hoshi University
    Hoshi University is a private Japanese university in Tokyo known for its specialized programs in pharmaceutical sciences and related health fields.
  • E. Misurata University
    Misurata University is a public higher education institution in the city of Misrata, Libya, offering a range of undergraduate and postgraduate programs across multiple disciplines.
  • 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_69d381bf3dc08190bf35a2643e4e8f22 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea2de4d48190aee65b3f6ec3cc48 completed April 7, 2026, 11:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7fc2160208190b6384190d9537df4 completed April 9, 2026, 7:21 p.m.
Created at: April 6, 2026, 12:12 p.m.