Triple

T2621067
Position Surface form Disambiguated ID Type / Status
Subject Gujarati people E59007 entity
Predicate traditionalDress P5541 FINISHED
Object Kediyu
Kediyu is a traditional flared upper garment worn by Gujarati men, especially in rural and folk dance contexts like Garba and Dandiya.
 E283251 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: Kediyu | Statement: [Gujarati people, traditionalDress, Kediyu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kediyu
Context triple: [Gujarati people, traditionalDress, Kediyu]
  • A. Kangar
    Kangar is the main administrative and commercial center of the Malaysian state of Perlis.
  • B. Tianguá
    Tianguá is a municipality in northeastern Brazil known for its location in the highlands of the state of Ceará and its role as a regional commercial and agricultural center.
  • C. Huating
    Huating is a historical town that once served as the name and administrative center of what is now Shanghai’s Songjiang District.
  • D. Chenoui
    Chenoui is a Berber (Amazigh) language variety spoken by the Shenwa people in northern Algeria.
  • E. Chuping
    Chuping is a town in the Malaysian state of Perlis, known for its extensive sugarcane plantations and hot climate.
  • 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: Kediyu
Triple: [Gujarati people, traditionalDress, Kediyu]
Generated description
Kediyu is a traditional flared upper garment worn by Gujarati men, especially in rural and folk dance contexts like Garba and Dandiya.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kediyu
Target entity description: Kediyu is a traditional flared upper garment worn by Gujarati men, especially in rural and folk dance contexts like Garba and Dandiya.
  • A. Kangar
    Kangar is the main administrative and commercial center of the Malaysian state of Perlis.
  • B. Tianguá
    Tianguá is a municipality in northeastern Brazil known for its location in the highlands of the state of Ceará and its role as a regional commercial and agricultural center.
  • C. Huating
    Huating is a historical town that once served as the name and administrative center of what is now Shanghai’s Songjiang District.
  • D. Chenoui
    Chenoui is a Berber (Amazigh) language variety spoken by the Shenwa people in northern Algeria.
  • E. Chuping
    Chuping is a town in the Malaysian state of Perlis, known for its extensive sugarcane plantations and hot climate.
  • 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_69ab4ac558388190962492cd2e1b0ce6 completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abd8993c4481908fecdb235e4e731c completed March 7, 2026, 7:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69af909918b88190905df6637ec0e412 completed March 10, 2026, 3:31 a.m.
NEDg Description generation batch_69af91625bd481908d3666af3cd3733f completed March 10, 2026, 3:34 a.m.
NED2 Entity disambiguation (via description) batch_69af91f9e1208190aa149c9afc84911c completed March 10, 2026, 3:37 a.m.
Created at: March 6, 2026, 9:50 p.m.