Triple

T14818416
Position Surface form Disambiguated ID Type / Status
Subject South Khorasan Province E348379 entity
Predicate hasCity P316 FINISHED
Object Sarayan
Sarayan is a small city in eastern Iran known as an administrative and agricultural center within South Khorasan Province.
 E1121538 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: Sarayan | Statement: [South Khorasan Province, hasCity, Sarayan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sarayan
Context triple: [South Khorasan Province, hasCity, Sarayan]
  • A. Sarina
    Sarina is a Dutch football manager and former player best known for coaching top international women’s national teams, including the Netherlands and England.
  • B. Sarina
    Sarina is a small coastal town and sugar-growing community in Queensland, Australia, located south of Mackay.
  • C. Sarneraa
    Sarneraa is a river in the canton of Obwalden in central Switzerland that drains Lake Lungern and flows northward toward Lake Lucerne.
  • D. Sakia
    Sakia is a prominent cultural center and arts venue in Cairo, Egypt, known for hosting concerts, exhibitions, and a wide range of cultural events.
  • E. Saravena
    Saravena is a Colombian town and municipality located in the northeastern oil-producing and conflict-affected region near the border with Venezuela.
  • 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: Sarayan
Triple: [South Khorasan Province, hasCity, Sarayan]
Generated description
Sarayan is a small city in eastern Iran known as an administrative and agricultural center within South Khorasan Province.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sarayan
Target entity description: Sarayan is a small city in eastern Iran known as an administrative and agricultural center within South Khorasan Province.
  • A. Sarina
    Sarina is a Dutch football manager and former player best known for coaching top international women’s national teams, including the Netherlands and England.
  • B. Sarina
    Sarina is a small coastal town and sugar-growing community in Queensland, Australia, located south of Mackay.
  • C. Sarneraa
    Sarneraa is a river in the canton of Obwalden in central Switzerland that drains Lake Lungern and flows northward toward Lake Lucerne.
  • D. Sakia
    Sakia is a prominent cultural center and arts venue in Cairo, Egypt, known for hosting concerts, exhibitions, and a wide range of cultural events.
  • E. Saravena
    Saravena is a Colombian town and municipality located in the northeastern oil-producing and conflict-affected region near the border with Venezuela.
  • 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_69d822eb8f588190bf53445e730a934f completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decfe4cf38819090f25ef045351d5d completed April 14, 2026, 11:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe389940e081908ad627955cb8d52e completed May 8, 2026, 7:25 p.m.
NEDg Description generation batch_69fe3a315d2c81908db44e7792908e39 completed May 8, 2026, 7:32 p.m.
NED2 Entity disambiguation (via description) batch_69fe3bda81cc8190b256ec6284383dde completed May 8, 2026, 7:39 p.m.
Created at: April 10, 2026, 1:50 a.m.