Triple

T7122224
Position Surface form Disambiguated ID Type / Status
Subject Mahanagar E165974 entity
Predicate basedOn P98 FINISHED
Object Abataranika
Abataranika is a Bengali literary work that served as the source material for the film "Mahanagar."
 E643186 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: Abataranika | Statement: [Mahanagar, basedOn, Abataranika]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Abataranika
Context triple: [Mahanagar, basedOn, Abataranika]
  • A. Gonaïves
    Gonaïves is a coastal city in western Haiti historically renowned as the birthplace of the nation’s independence and a center of political activity.
  • B. Carricart
    Carricart is a Spanish-language surname of likely Basque origin borne by individuals such as María del Carmen Cerruti Carricart.
  • C. Gibara
    Gibara is a small coastal town in northeastern Cuba known for its colonial architecture, fishing traditions, and annual international film festival.
  • D. Aibonito
    Aibonito is a mountainous municipality in central Puerto Rico known for its cool climate and flower festival.
  • E. Bayamo
    Bayamo is one of Cuba’s oldest colonial cities, historically significant as an early Spanish settlement and a center of Cuban independence sentiment.
  • 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: Abataranika
Triple: [Mahanagar, basedOn, Abataranika]
Generated description
Abataranika is a Bengali literary work that served as the source material for the film "Mahanagar."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Abataranika
Target entity description: Abataranika is a Bengali literary work that served as the source material for the film "Mahanagar."
  • A. Gonaïves
    Gonaïves is a coastal city in western Haiti historically renowned as the birthplace of the nation’s independence and a center of political activity.
  • B. Carricart
    Carricart is a Spanish-language surname of likely Basque origin borne by individuals such as María del Carmen Cerruti Carricart.
  • C. Gibara
    Gibara is a small coastal town in northeastern Cuba known for its colonial architecture, fishing traditions, and annual international film festival.
  • D. Aibonito
    Aibonito is a mountainous municipality in central Puerto Rico known for its cool climate and flower festival.
  • E. Bayamo
    Bayamo is one of Cuba’s oldest colonial cities, historically significant as an early Spanish settlement and a center of Cuban independence sentiment.
  • 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_69c6888227bc8190a1394679e3116f90 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e6493fd88190b0c066a2ad74917c completed March 27, 2026, 8:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7a32e8098819090b88fc920416f6b completed March 28, 2026, 9:45 a.m.
NEDg Description generation batch_69c7a3f2b51c81909f058149e9bd9f0a completed March 28, 2026, 9:48 a.m.
NED2 Entity disambiguation (via description) batch_69c7a4a9e91881909df07f1c540f191e completed March 28, 2026, 9:51 a.m.
Created at: March 27, 2026, 2:44 p.m.