Triple

T8280495
Position Surface form Disambiguated ID Type / Status
Subject Lot E193657 entity
Predicate nameInArabic P6450 FINISHED
Object Lut E157953 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: Lut | Statement: [Lot, nameInArabic, Lut]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lut
Context triple: [Lot, nameInArabic, Lut]
  • A. Lut chosen
    Lut is a prophet in Islamic tradition, known for being sent to the people of Sodom and Gomorrah and warning them against their immoral behavior.
  • B. Lutayan
    Lutayan is a municipality in the province of Sultan Kudarat in the Philippines, known for its agricultural economy and proximity to Lake Buluan.
  • C. Liluah
    Liluah is a suburban locality in the Howrah district of West Bengal, India, known for its residential areas and railway facilities near Kolkata.
  • D. Lugana
    Lugana is an Italian white wine appellation near Lake Garda, renowned for its fresh, mineral-driven wines primarily made from the Turbiana grape.
  • E. Luga
    Luga is a small historic town in northwestern Russia known for its strategic location and role in regional transport and industry.
  • 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_69ca82e217a48190880695635c44b2ed completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb79ee66e48190af7058b14f3daac9 completed March 31, 2026, 7:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd686d9be081908b0490e708f51ad7 completed April 1, 2026, 6:48 p.m.
Created at: March 30, 2026, 5:51 p.m.