Triple

T13249863
Position Surface form Disambiguated ID Type / Status
Subject Turkish Armed Forces rank structure E315497 entity
Predicate hasFieldOfficerRank P44633 FINISHED
Object Yarbay
Yarbay is a mid-level field officer rank in the Turkish Armed Forces, equivalent to a lieutenant colonel in many other military organizations.
 E1029834 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: Yarbay | Statement: [Turkish Armed Forces rank structure, hasFieldOfficerRank, Yarbay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yarbay
Context triple: [Turkish Armed Forces rank structure, hasFieldOfficerRank, Yarbay]
  • A. Yura
    Yura is a common Slavic diminutive form of the male given name Yuri (or Yuriy), often used as a familiar or affectionate nickname.
  • B. Yaropolch
    Yaropolch is a village in Russia historically noted as the place where Ukrainian Hetman Petro Doroshenko died.
  • C. Yelshanka
    Yelshanka is a locality in Russia best known as the namesake of Yelshanka railway station.
  • D. Yamskaya
    Yamskaya is a name element associated with several historic streets and districts in Moscow, traditionally linked to coachmen’s settlements along major travel routes.
  • E. Vishkanya
    Vishkanya is a 1991 Indian Hindi-language horror film known for its supernatural revenge plot and early appearance of actress Riya Sen.
  • 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: Yarbay
Triple: [Turkish Armed Forces rank structure, hasFieldOfficerRank, Yarbay]
Generated description
Yarbay is a mid-level field officer rank in the Turkish Armed Forces, equivalent to a lieutenant colonel in many other military organizations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yarbay
Target entity description: Yarbay is a mid-level field officer rank in the Turkish Armed Forces, equivalent to a lieutenant colonel in many other military organizations.
  • A. Yura
    Yura is a common Slavic diminutive form of the male given name Yuri (or Yuriy), often used as a familiar or affectionate nickname.
  • B. Yaropolch
    Yaropolch is a village in Russia historically noted as the place where Ukrainian Hetman Petro Doroshenko died.
  • C. Yelshanka
    Yelshanka is a locality in Russia best known as the namesake of Yelshanka railway station.
  • D. Yamskaya
    Yamskaya is a name element associated with several historic streets and districts in Moscow, traditionally linked to coachmen’s settlements along major travel routes.
  • E. Vishkanya
    Vishkanya is a 1991 Indian Hindi-language horror film known for its supernatural revenge plot and early appearance of actress Riya Sen.
  • 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_69d806b1072881909e46bd212259c5f0 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dadcd4cb008190af99c4856e76ac08 completed April 11, 2026, 11:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69f70a3b8ca48190863aff25f12d0e7e completed May 3, 2026, 8:41 a.m.
NEDg Description generation batch_69f70b37ebe081909ed2ae0f42ccad2d completed May 3, 2026, 8:45 a.m.
NED2 Entity disambiguation (via description) batch_69f70ca5b2a88190b5f886474f674e84 completed May 3, 2026, 8:51 a.m.
Created at: April 9, 2026, 9:24 p.m.