Triple

T9461297
Position Surface form Disambiguated ID Type / Status
Subject Khujand E228149 entity
Predicate formerName P65 FINISHED
Object Leninabad
Leninabad was the Soviet-era name of the city now known as Khujand, a major historical and industrial center in northern Tajikistan.
 E804179 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: Leninabad | Statement: [Khujand, formerName, Leninabad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Leninabad
Context triple: [Khujand, formerName, Leninabad]
  • A. Astarabad
    Astarabad is the historical name of the city now known as Gorgan in northeastern Iran, once an important regional center near the Caspian Sea.
  • B. Maidan Shahr
    Maidan Shahr is a town in central Afghanistan that serves as the administrative and commercial hub of Wardak Province.
  • C. Babol
    Babol is a prominent city in northern Iran known for its historical significance, dense population, and location near the Caspian Sea in Mazandaran Province.
  • D. Taşkent
    Taşkent is a small mountainous district and town in Turkey’s Konya Province, known for its rural character and scenic Anatolian landscape.
  • E. Nazarabad
    Nazarabad is a city in Iran that serves as an important urban center within Alborz Province.
  • 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: Leninabad
Triple: [Khujand, formerName, Leninabad]
Generated description
Leninabad was the Soviet-era name of the city now known as Khujand, a major historical and industrial center in northern Tajikistan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Leninabad
Target entity description: Leninabad was the Soviet-era name of the city now known as Khujand, a major historical and industrial center in northern Tajikistan.
  • A. Astarabad
    Astarabad is the historical name of the city now known as Gorgan in northeastern Iran, once an important regional center near the Caspian Sea.
  • B. Maidan Shahr
    Maidan Shahr is a town in central Afghanistan that serves as the administrative and commercial hub of Wardak Province.
  • C. Babol
    Babol is a prominent city in northern Iran known for its historical significance, dense population, and location near the Caspian Sea in Mazandaran Province.
  • D. Taşkent
    Taşkent is a small mountainous district and town in Turkey’s Konya Province, known for its rural character and scenic Anatolian landscape.
  • E. Nazarabad
    Nazarabad is a city in Iran that serves as an important urban center within Alborz Province.
  • 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_69ca843b123881909b0e60028475d12d completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd7fcc8b1881908aa6ee13ab195330 completed April 1, 2026, 8:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69d139e6cc888190a2175149c59bb138 completed April 4, 2026, 4:18 p.m.
NEDg Description generation batch_69d13bc8ce4081909a58db4014f2748d completed April 4, 2026, 4:26 p.m.
NED2 Entity disambiguation (via description) batch_69d13c58beb08190ab41485bc7dd9b6d completed April 4, 2026, 4:29 p.m.
Created at: March 30, 2026, 7:52 p.m.