Triple

T21939358
Position Surface form Disambiguated ID Type / Status
Subject Lahore film studios E541777 entity
Predicate locatedIn P40 FINISHED
Object Lahore NE NERFINISHED

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: Lahore | Statement: [Lahore film studios, locatedIn, Lahore]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lahore
Context triple: [Lahore film studios, locatedIn, Lahore]
  • A. Lahore chosen
    Lahore is a major cultural, historical, and economic center of Pakistan, known for its rich Mughal heritage, educational institutions, and role in the region’s political history.
  • B. Rawalpindi
    Rawalpindi is a major city in Pakistan’s Punjab province, historically significant as a former temporary national capital and now a key commercial and military center.
  • C. Faisalabad
    Faisalabad is a major industrial city in Pakistan’s Punjab province, known especially for its large textile industry and role as a commercial hub.
  • D. Karachi
    Karachi is Pakistan’s sprawling economic hub and major port city on the Arabian Sea, known for its diverse population and central role in the country’s finance, industry, and culture.
  • E. Peshawar
    Peshawar is one of Pakistan’s oldest and largest cities, a historic cultural and economic hub located near the Khyber Pass in the country’s northwest.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c47e2e5c81909a7f74ce3de50911 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f1241f582c81909a244419cec38b19 completed April 28, 2026, 9:18 p.m.
Created at: April 16, 2026, 7:55 p.m.