Triple

T13168675
Position Surface form Disambiguated ID Type / Status
Subject Singori sweet E312917 entity
Predicate isKnownBy P27854 FINISHED
Object Singauri
Singauri is a traditional sweet from the Kumaon region of Uttarakhand, India, typically made of khoya (reduced milk) wrapped in maalu leaves and known for its rich, aromatic flavor.
 E1025457 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: Singauri | Statement: [Singori sweet, isKnownBy, Singauri]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Singauri
Context triple: [Singori sweet, isKnownBy, Singauri]
  • A. Singa
    Singa is a city in southeastern Sudan that serves as the administrative and economic center of Sennar State along the Blue Nile.
  • B. Jitra
    Jitra is a town in the state of Kedah in northern Peninsular Malaysia, historically noted as the site of a major World War II battle between British Commonwealth and Japanese forces.
  • C. Rangloi
    Rangloi is a regional dialect of the Kumaoni language spoken in parts of the Indian Himalayan region.
  • D. Sawanih
    Sawanih is a notable literary work by the Indian poet Faizi, recognized for its contribution to classical Persian literature in South Asia.
  • E. Noukadubi
    Noukadubi is a 2011 Bengali-language film adaptation of Rabindranath Tagore’s novel of the same name, directed by Rituparno Ghosh.
  • 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: Singauri
Triple: [Singori sweet, isKnownBy, Singauri]
Generated description
Singauri is a traditional sweet from the Kumaon region of Uttarakhand, India, typically made of khoya (reduced milk) wrapped in maalu leaves and known for its rich, aromatic flavor.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Singauri
Target entity description: Singauri is a traditional sweet from the Kumaon region of Uttarakhand, India, typically made of khoya (reduced milk) wrapped in maalu leaves and known for its rich, aromatic flavor.
  • A. Singa
    Singa is a city in southeastern Sudan that serves as the administrative and economic center of Sennar State along the Blue Nile.
  • B. Jitra
    Jitra is a town in the state of Kedah in northern Peninsular Malaysia, historically noted as the site of a major World War II battle between British Commonwealth and Japanese forces.
  • C. Rangloi
    Rangloi is a regional dialect of the Kumaoni language spoken in parts of the Indian Himalayan region.
  • D. Sawanih
    Sawanih is a notable literary work by the Indian poet Faizi, recognized for its contribution to classical Persian literature in South Asia.
  • E. Noukadubi
    Noukadubi is a 2011 Bengali-language film adaptation of Rabindranath Tagore’s novel of the same name, directed by Rituparno Ghosh.
  • 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_69d806ac3ee081909b2fd27d060aa974 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c2e03c481909909b8f10c7e8ffc completed April 10, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6eaf92c1881909d387dcf50d8d09f completed May 3, 2026, 6:28 a.m.
NEDg Description generation batch_69f6f11b77a081909ea2ddedbac5abb8 completed May 3, 2026, 6:54 a.m.
NED2 Entity disambiguation (via description) batch_69f6f1b5c67c8190a2216ef32c5420c9 completed May 3, 2026, 6:56 a.m.
Created at: April 9, 2026, 9:13 p.m.