Triple

T14026082
Position Surface form Disambiguated ID Type / Status
Subject Lal Tibba E337459 entity
Predicate near P350 FINISHED
Object Landour E337462 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: Landour | Statement: [Lal Tibba, near, Landour]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Landour
Context triple: [Lal Tibba, near, Landour]
  • A. Landour chosen
    Landour is a quiet, colonial-era hill cantonment in Uttarakhand, India, known for its scenic Himalayan views, old-world charm, and literary connections.
  • B. Magarpatta
    Magarpatta is a planned integrated township in Pune, India, known for its large IT park, residential complexes, and commercial infrastructure.
  • C. Hubli
    Hubli is a major commercial and transportation hub in the Indian state of Karnataka, known for its busy railway junction and growing industrial economy.
  • D. Kalady
    Kalady is a village in the Indian state of Kerala, revered as the birthplace of the 8th-century philosopher and theologian Adi Shankaracharya and an important Hindu pilgrimage center.
  • E. Coorg
    Coorg, also known as Kodagu, is a scenic hill district in Karnataka, India, famed for its coffee plantations, lush forests, and mist-covered landscapes.
  • 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_69d81c6543a48190bd5ba93d7419e797 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2fa6ca7481908976ce748a1957b1 completed April 14, 2026, 12:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbc333b7a08190b4f121fef69f7513 completed May 6, 2026, 10:39 p.m.
Created at: April 9, 2026, 10:20 p.m.