Triple

T8366144
Position Surface form Disambiguated ID Type / Status
Subject Sassoun E197130 entity
Predicate knownAs P39 FINISHED
Object Sason
Sason is a town and district in Batman Province in southeastern Turkey, historically known as Sassoun and noted for its Armenian cultural heritage and mountainous terrain.
 E728080 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: Sason | Statement: [Sassoun, knownAs, Sason]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sason
Context triple: [Sassoun, knownAs, Sason]
  • A. Sason
    Sason is a surname most notably associated with Swedish industrial designer Sixten Sason, known for his influential work with Saab automobiles.
  • B. Sarnıç
    Sarnıç is a short story collection by renowned Turkish writer Sait Faik Abasıyanık, known for its vivid portrayals of everyday life and marginalized characters in Istanbul.
  • C. Sarikoli
    Sarikoli is an Eastern Iranian language spoken primarily by the Tajik ethnic community in the Tashkurgan region of Xinjiang, China.
  • D. Demerdzhi
    Demerdzhi is a notable mountain massif in Crimea, famous for its striking rock formations and scenic landscapes.
  • E. Teberda
    Teberda is a small town in the North Caucasus region of Russia, known as a gateway to the surrounding mountains and protected natural areas.
  • 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: Sason
Triple: [Sassoun, knownAs, Sason]
Generated description
Sason is a town and district in Batman Province in southeastern Turkey, historically known as Sassoun and noted for its Armenian cultural heritage and mountainous terrain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sason
Target entity description: Sason is a town and district in Batman Province in southeastern Turkey, historically known as Sassoun and noted for its Armenian cultural heritage and mountainous terrain.
  • A. Sason
    Sason is a surname most notably associated with Swedish industrial designer Sixten Sason, known for his influential work with Saab automobiles.
  • B. Sarnıç
    Sarnıç is a short story collection by renowned Turkish writer Sait Faik Abasıyanık, known for its vivid portrayals of everyday life and marginalized characters in Istanbul.
  • C. Sarikoli
    Sarikoli is an Eastern Iranian language spoken primarily by the Tajik ethnic community in the Tashkurgan region of Xinjiang, China.
  • D. Demerdzhi
    Demerdzhi is a notable mountain massif in Crimea, famous for its striking rock formations and scenic landscapes.
  • E. Teberda
    Teberda is a small town in the North Caucasus region of Russia, known as a gateway to the surrounding mountains and protected natural areas.
  • 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_69ca82f2dbe48190aba982e75a0d94de completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb808cf80c8190941c3cc0248a5df2 completed March 31, 2026, 8:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69cdc78c0c208190ba590c74512a4043 completed April 2, 2026, 1:34 a.m.
NEDg Description generation batch_69cdcc88456c8190ba8613b4cbf40fbb completed April 2, 2026, 1:55 a.m.
NED2 Entity disambiguation (via description) batch_69cdcd75714881908f0b069a94ee334f completed April 2, 2026, 1:59 a.m.
Created at: March 30, 2026, 6 p.m.