Triple

T20180593
Position Surface form Disambiguated ID Type / Status
Subject Lalitpur district E492714 entity
Predicate hasNotableTown P14082 FINISHED
Object Talbehat
Talbehat is a town in the Indian state of Uttar Pradesh known for its historic fort, scenic lake, and location along the Jhansi–Kanpur railway line.
 E1417277 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: Talbehat | Statement: [Lalitpur district, hasNotableTown, Talbehat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Talbehat
Context triple: [Lalitpur district, hasNotableTown, Talbehat]
  • A. Tabasaran
    Tabasaran is a Northeast Caucasian language spoken primarily by the Tabasaran people in southern Dagestan, Russia.
  • B. Las Tablas
    Las Tablas is a prominent town in Panama known as a cultural center of the Azuero Peninsula, especially famous for its vibrant traditional festivals and folklore.
  • C. Dastar
    Dastar is a traditional Sikh turban that serves as both a religious symbol and a cultural marker of identity and honor.
  • D. El Tebbin
    El Tebbin is an industrial district in southern Cairo, Egypt, known for its steel and heavy manufacturing facilities.
  • E. Tabiriyya
    Tabiriyya is a sub-school within the Zaydi branch of Shia Islam, representing a distinct theological and legal tradition in that sect.
  • 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: Talbehat
Triple: [Lalitpur district, hasNotableTown, Talbehat]
Generated description
Talbehat is a town in the Indian state of Uttar Pradesh known for its historic fort, scenic lake, and location along the Jhansi–Kanpur railway line.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Talbehat
Target entity description: Talbehat is a town in the Indian state of Uttar Pradesh known for its historic fort, scenic lake, and location along the Jhansi–Kanpur railway line.
  • A. Tabasaran
    Tabasaran is a Northeast Caucasian language spoken primarily by the Tabasaran people in southern Dagestan, Russia.
  • B. Las Tablas
    Las Tablas is a prominent town in Panama known as a cultural center of the Azuero Peninsula, especially famous for its vibrant traditional festivals and folklore.
  • C. Dastar
    Dastar is a traditional Sikh turban that serves as both a religious symbol and a cultural marker of identity and honor.
  • D. El Tebbin
    El Tebbin is an industrial district in southern Cairo, Egypt, known for its steel and heavy manufacturing facilities.
  • E. Tabiriyya
    Tabiriyya is a sub-school within the Zaydi branch of Shia Islam, representing a distinct theological and legal tradition in that sect.
  • 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_69da6268a034819081cbd9ea5a1c9475 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e668eed2e88190b54b15e6545dbdf8 completed April 20, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a083c7b9fa08190bbf3c3da21ab21ba completed May 16, 2026, 9:44 a.m.
NEDg Description generation batch_6a084094eccc8190834a1335f3b70411 completed May 16, 2026, 10:01 a.m.
NED2 Entity disambiguation (via description) batch_6a0841a3f8e0819099c13e76d5f4285d completed May 16, 2026, 10:06 a.m.
Created at: April 11, 2026, 11:36 p.m.