Triple

T3791370
Position Surface form Disambiguated ID Type / Status
Subject Ziya Gökalp E89655 entity
Predicate givenName P17 FINISHED
Object Ziya
Ziya is a masculine given name of Turkish origin, historically associated with notable figures such as sociologist and nationalist thinker Ziya Gökalp.
 E388860 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: Ziya | Statement: [Ziya Gökalp, givenName, Ziya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ziya
Context triple: [Ziya Gökalp, givenName, Ziya]
  • A. Ahmet
    Ahmet is a common male given name of Arabic origin, widely used in Turkey and other Muslim-majority countries as a variant of Ahmed.
  • B. Seyhun
    Seyhun is the historical name used in Islamic and Central Asian sources for the Syr Darya River, one of the major rivers of Central Asia.
  • C. Ersoy
    Ersoy is a Turkish surname most notably borne by Mehmet Akif Ersoy, the poet of the Turkish National Anthem.
  • D. Gazi
    Gazi is an honorific title in Turkey, historically bestowed for distinguished military valor and sacrifice in war.
  • E. Güntekin
    Güntekin is the surname of the renowned Turkish novelist and playwright Reşat Nuri, best known for works such as "Çalıkuşu."
  • 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: Ziya
Triple: [Ziya Gökalp, givenName, Ziya]
Generated description
Ziya is a masculine given name of Turkish origin, historically associated with notable figures such as sociologist and nationalist thinker Ziya Gökalp.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ziya
Target entity description: Ziya is a masculine given name of Turkish origin, historically associated with notable figures such as sociologist and nationalist thinker Ziya Gökalp.
  • A. Ahmet
    Ahmet is a common male given name of Arabic origin, widely used in Turkey and other Muslim-majority countries as a variant of Ahmed.
  • B. Seyhun
    Seyhun is the historical name used in Islamic and Central Asian sources for the Syr Darya River, one of the major rivers of Central Asia.
  • C. Ersoy
    Ersoy is a Turkish surname most notably borne by Mehmet Akif Ersoy, the poet of the Turkish National Anthem.
  • D. Gazi
    Gazi is an honorific title in Turkey, historically bestowed for distinguished military valor and sacrifice in war.
  • E. Güntekin
    Güntekin is the surname of the renowned Turkish novelist and playwright Reşat Nuri, best known for works such as "Çalıkuşu."
  • 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_69aed9597d6881909b6ee3b9de859223 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aee7687c4481908d5e6988e2638b68 completed March 9, 2026, 3:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4f055e3988190a1b3633e390c0d6f completed March 14, 2026, 5:21 a.m.
NEDg Description generation batch_69b4f2094c988190a812194ef5598cdf completed March 14, 2026, 5:28 a.m.
NED2 Entity disambiguation (via description) batch_69b4f26aee948190b95c94801f0959ec completed March 14, 2026, 5:30 a.m.
Created at: March 9, 2026, 3:15 p.m.