Triple

T11424755
Position Surface form Disambiguated ID Type / Status
Subject Rishi Kapoor E270718 entity
Predicate notableWork P4 FINISHED
Object Henna
Henna is a 1991 Indian romantic drama film directed by Randhir Kapoor, known for its cross-border love story set between India and Pakistan.
 E924723 NE FINISHED

How this triple was built (4 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: Henna | Statement: [Rishi Kapoor, notableWork, Henna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Henna
Context triple: [Rishi Kapoor, notableWork, Henna]
  • A. Tinte
    Tinte is a small village in the Dutch province of South Holland, known for its rural character and annual local festivities.
  • B. Surma
    Surma is a group of closely related languages spoken by indigenous communities in southwestern Ethiopia and neighboring regions.
  • C. War Paint
    War Paint is a Broadway musical that dramatizes the rivalry between cosmetics titans Helena Rubinstein and Elizabeth Arden in mid-20th-century America.
  • D. Neela
    Neela is a prominent commander in the monkey kingdom of Kishkindha in the Indian epic Ramayana, known for his leadership in Rama’s campaign against Ravana.
  • E. Neela
    Neela is a central street racer and love interest in the film "The Fast and the Furious: Tokyo Drift," known for her drifting skills in Tokyo's underground racing scene.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Henna
Triple: [Rishi Kapoor, notableWork, Henna]
Generated description
Henna is a 1991 Indian romantic drama film directed by Randhir Kapoor, known for its cross-border love story set between India and Pakistan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Henna
Target entity description: Henna is a 1991 Indian romantic drama film directed by Randhir Kapoor, known for its cross-border love story set between India and Pakistan.
  • A. Tinte
    Tinte is a small village in the Dutch province of South Holland, known for its rural character and annual local festivities.
  • B. Surma
    Surma is a group of closely related languages spoken by indigenous communities in southwestern Ethiopia and neighboring regions.
  • C. War Paint
    War Paint is a Broadway musical that dramatizes the rivalry between cosmetics titans Helena Rubinstein and Elizabeth Arden in mid-20th-century America.
  • D. Neela
    Neela is a prominent commander in the monkey kingdom of Kishkindha in the Indian epic Ramayana, known for his leadership in Rama’s campaign against Ravana.
  • E. Neela
    Neela is a central street racer and love interest in the film "The Fast and the Furious: Tokyo Drift," known for her drifting skills in Tokyo's underground racing scene.
  • F. None of above. chosen

Provenance (5 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_69d6aadeef688190874bcecd88b3dd9b completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d806be9c2c819084da13101cbb6c81 completed April 9, 2026, 8:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5b8b553808190bf8b40d9b03e12b7 completed April 20, 2026, 5:25 a.m.
NEDg Description generation batch_69e5c28e2dd481909b45a43b5825f393 completed April 20, 2026, 6:07 a.m.
NED2 Entity disambiguation (via description) batch_69e5c4722c348190a4c49edb1f6df240 completed April 20, 2026, 6:15 a.m.
Created at: April 8, 2026, 9:35 p.m.