Triple

T10794824
Position Surface form Disambiguated ID Type / Status
Subject Zanjan Province E254676 entity
Predicate hasCity P316 FINISHED
Object Mahneshan
Mahneshan is a small city in northwestern Iran known for its rural surroundings and location within Zanjan Province.
E887968 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: Mahneshan | Statement: [Zanjan Province, hasCity, Mahneshan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mahneshan
Context triple: [Zanjan Province, hasCity, Mahneshan]
  • A. Shahrud
    Shahrud is a major city in northeastern Iran known as an important regional hub for transportation, agriculture, and access to nearby natural attractions such as the Alborz Mountains and desert landscapes.
  • B. Taleqan
    Taleqan is a small mountainous city in northern Iran known for its cool climate, natural landscapes, and traditional rural architecture.
  • C. Azarbarzin
    Azarbarzin is a character from Persian epic tradition, known primarily as the son of the legendary hero Esfandiyar.
  • D. Andimeshk
    Andimeshk is a city in southwestern Iran known as a regional transportation hub and gateway to the Zagros Mountains.
  • E. Parsa
    Parsa is the ancient name of the Persian people and their homeland, from which the ethnonym "Persian" is historically derived.
  • 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: Mahneshan
Triple: [Zanjan Province, hasCity, Mahneshan]
Generated description
Mahneshan is a small city in northwestern Iran known for its rural surroundings and location within Zanjan Province.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mahneshan
Target entity description: Mahneshan is a small city in northwestern Iran known for its rural surroundings and location within Zanjan Province.
  • A. Shahrud
    Shahrud is a major city in northeastern Iran known as an important regional hub for transportation, agriculture, and access to nearby natural attractions such as the Alborz Mountains and desert landscapes.
  • B. Taleqan
    Taleqan is a small mountainous city in northern Iran known for its cool climate, natural landscapes, and traditional rural architecture.
  • C. Azarbarzin
    Azarbarzin is a character from Persian epic tradition, known primarily as the son of the legendary hero Esfandiyar.
  • D. Andimeshk
    Andimeshk is a city in southwestern Iran known as a regional transportation hub and gateway to the Zagros Mountains.
  • E. Parsa
    Parsa is the ancient name of the Persian people and their homeland, from which the ethnonym "Persian" is historically derived.
  • 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_69d6aa61c15c8190a1839550c56e75e1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d733321dd881909dcd4224dfa9822a completed April 9, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69de84f1fdfc8190a31a13ae434e56c1 completed April 14, 2026, 6:18 p.m.
NEDg Description generation batch_69de8e6f3fac8190bcd1675978d6d6d7 completed April 14, 2026, 6:58 p.m.
NED2 Entity disambiguation (via description) batch_69de8fa679cc81909cb51035e5403ce9 completed April 14, 2026, 7:04 p.m.
Created at: April 8, 2026, 9:17 p.m.