Triple

T5792145
Position Surface form Disambiguated ID Type / Status
Subject Doordarshan E128419 entity
Predicate notableProgram P4 FINISHED
Object Buniyaad
Buniyaad is a landmark Indian television drama series that aired in the late 1980s, depicting the impact of the Partition of India on a Punjabi family.
 E546429 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: Buniyaad | Statement: [Doordarshan, notableProgram, Buniyaad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Buniyaad
Context triple: [Doordarshan, notableProgram, Buniyaad]
  • A. Mazar
    Mazar is the surname of American actress and television personality Debi Mazar, known for her sharp-tongued roles in film and TV.
  • B. Bisher Bashi
    Bisher Bashi is a renowned Bengali poetry collection by Kazi Nazrul Islam, noted for its intense emotional expression and revolutionary themes.
  • C. Baabda
    Baabda is a town in Lebanon that serves as the administrative center of the Mount Lebanon Governorate and hosts the Lebanese presidential palace.
  • D. Multazam
    Multazam is the small sacred area between the Black Stone and the door of the Kaaba where pilgrims supplicate, believing prayers there are especially accepted.
  • E. El Tebbin
    El Tebbin is an industrial district in southern Cairo, Egypt, known for its steel and heavy manufacturing facilities.
  • 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: Buniyaad
Triple: [Doordarshan, notableProgram, Buniyaad]
Generated description
Buniyaad is a landmark Indian television drama series that aired in the late 1980s, depicting the impact of the Partition of India on a Punjabi family.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Buniyaad
Target entity description: Buniyaad is a landmark Indian television drama series that aired in the late 1980s, depicting the impact of the Partition of India on a Punjabi family.
  • A. Mazar
    Mazar is the surname of American actress and television personality Debi Mazar, known for her sharp-tongued roles in film and TV.
  • B. Bisher Bashi
    Bisher Bashi is a renowned Bengali poetry collection by Kazi Nazrul Islam, noted for its intense emotional expression and revolutionary themes.
  • C. Baabda
    Baabda is a town in Lebanon that serves as the administrative center of the Mount Lebanon Governorate and hosts the Lebanese presidential palace.
  • D. Multazam
    Multazam is the small sacred area between the Black Stone and the door of the Kaaba where pilgrims supplicate, believing prayers there are especially accepted.
  • E. El Tebbin
    El Tebbin is an industrial district in southern Cairo, Egypt, known for its steel and heavy manufacturing facilities.
  • 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_69c00845ca68819081a2ce3ecca577f7 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02a5870b88190bbfaac2782635128 completed March 22, 2026, 5:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c09824c7f0819095565e0f29b3a508 completed March 23, 2026, 1:32 a.m.
NEDg Description generation batch_69c098a0325c81909a1326b94e40ed50 completed March 23, 2026, 1:34 a.m.
NED2 Entity disambiguation (via description) batch_69c09943deec819085992c4e44050a34 completed March 23, 2026, 1:37 a.m.
Created at: March 22, 2026, 3:51 p.m.