Triple

T11963118
Position Surface form Disambiguated ID Type / Status
Subject Boni MRT station E284719 entity
Predicate hasStationCode P1289 FINISHED
Object BON E501454 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: BON | Statement: [Boni MRT station, hasStationCode, BON]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: BON
Context triple: [Boni MRT station, hasStationCode, BON]
  • A. BON chosen
    BON is the National Rail station code for Bolton railway station in Greater Manchester, England.
  • B. Bonne
    Bonne of Berry was a 14th-century French noblewoman of the House of Valois, daughter of John II of France and a politically significant figure through her dynastic marriages.
  • C. Bon
    Bon is an ancient Tibetan spiritual tradition and religion, distinct from but historically intertwined with Buddhism, that encompasses rituals, cosmology, and practices rooted in the pre-Buddhist culture of Tibet.
  • D. BOL
    BOL is the three-letter ISO 3166-1 alpha-3 country code assigned to Bolivia.
  • E. Bun
    Bun is a modern, high-performance JavaScript runtime and toolkit designed as an alternative to Node.js and Deno, featuring a built-in bundler, test runner, and package manager.
  • 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_69d6ab2eaeb881909f7914758f859413 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9037848f481908276716675464464 completed April 10, 2026, 2:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69f4594054f08190b28b35f62dfb9198 completed May 1, 2026, 7:41 a.m.
Created at: April 8, 2026, 9:45 p.m.