Triple

T8819209
Position Surface form Disambiguated ID Type / Status
Subject Losar E209857 entity
Predicate celebratedIn P7822 FINISHED
Object Darjeeling E162673 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: Darjeeling | Statement: [Losar, celebratedIn, Darjeeling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Darjeeling
Context triple: [Losar, celebratedIn, Darjeeling]
  • A. Darjeeling chosen
    Darjeeling is a famous hill station in the Indian Himalayas renowned for its tea plantations, scenic mountain views, and colonial-era charm.
  • B. Ranikhet
    Ranikhet is a hill station and cantonment town in the Kumaon region of Uttarakhand, India, known for its scenic Himalayan views and pleasant climate.
  • C. Jalpaiguri
    Jalpaiguri is a town in northeastern India known as an important administrative and commercial center near the Himalayan foothills.
  • D. Nainital
    Nainital is a popular hill station and lake town in northern India, known for its scenic beauty and colonial-era charm.
  • E. Mussoorie
    Mussoorie is a popular hill station in the Indian state of Uttarakhand, known for its scenic Himalayan views, colonial-era architecture, and role as a major educational and administrative training hub.
  • 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_69ca8364e13081909c85fe80f44fe86f completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc600e6e2881908934cbff0ab5d6fd completed April 1, 2026, midnight
NED1 Entity disambiguation (via context triple) batch_69cf6fc96a8481909d09835845ccdcbf completed April 3, 2026, 7:44 a.m.
Created at: March 30, 2026, 6:46 p.m.