Triple

T5461865
Position Surface form Disambiguated ID Type / Status
Subject Pakistani cinema E122610 entity
Predicate alsoKnownAs P39 FINISHED
Object Lollywood E125358 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: Lollywood | Statement: [Pakistani cinema, alsoKnownAs, Lollywood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lollywood
Context triple: [Pakistani cinema, alsoKnownAs, Lollywood]
  • A. Lollywood chosen
    Lollywood is the Pakistani film industry based in Lahore, historically known for producing Punjabi- and Urdu-language movies.
  • B. Nollywood
    Nollywood is Nigeria’s prolific film industry, renowned as one of the largest movie producers in the world and a major cultural force across Africa.
  • C. Tollywood
    Tollywood is the Bengali-language film industry based primarily in Kolkata, India, known for its rich artistic and literary cinematic tradition.
  • D. Bollywood cinema
    Bollywood cinema is the mainstream Hindi-language film industry based in Mumbai, India, known for its song-and-dance musicals, melodrama, and massive cultural influence across South Asia and the global Indian diaspora.
  • E. Wellywood
    Wellywood is a playful nickname for Wellington, New Zealand, referencing its prominent film industry and association with director Peter Jackson.
  • 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_69bd4643f16081908d7f29e08096115a completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd9201dbfc8190bea22d6ecbc25b3e completed March 20, 2026, 6:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf414ebd288190ae90593232ff2db9 completed March 22, 2026, 1:09 a.m.
Created at: March 20, 2026, 2:08 p.m.