Triple

T7840494
Position Surface form Disambiguated ID Type / Status
Subject İzmir Province E181790 entity
Predicate contains P35 FINISHED
Object Beydağ
Beydağ is a small town and district in western Turkey known for its agricultural landscape and location within İzmir Province.
 E701929 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: Beydağ | Statement: [İzmir Province, contains, Beydağ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Beydağ
Context triple: [İzmir Province, contains, Beydağ]
  • A. Elmadağ
    Elmadağ is a district and town in central Turkey known for its mountainous terrain and proximity to the capital city, Ankara.
  • B. Yanardag
    Yanardag is a natural gas fire that continuously blazes on a hillside near Baku, Azerbaijan, and is one of the country’s most famous “Land of Fire” attractions.
  • C. Palandöken
    Palandöken is a district and popular ski resort area in eastern Turkey, located near the city of Erzurum in Erzurum Province.
  • D. Büyükerşen
    Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
  • E. Baltalimanı
    Baltalimanı is a coastal neighborhood along the Bosphorus in Istanbul, known for its scenic waterfront and residential character.
  • 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: Beydağ
Triple: [İzmir Province, contains, Beydağ]
Generated description
Beydağ is a small town and district in western Turkey known for its agricultural landscape and location within İzmir Province.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Beydağ
Target entity description: Beydağ is a small town and district in western Turkey known for its agricultural landscape and location within İzmir Province.
  • A. Elmadağ
    Elmadağ is a district and town in central Turkey known for its mountainous terrain and proximity to the capital city, Ankara.
  • B. Yanardag
    Yanardag is a natural gas fire that continuously blazes on a hillside near Baku, Azerbaijan, and is one of the country’s most famous “Land of Fire” attractions.
  • C. Palandöken
    Palandöken is a district and popular ski resort area in eastern Turkey, located near the city of Erzurum in Erzurum Province.
  • D. Büyükerşen
    Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
  • E. Baltalimanı
    Baltalimanı is a coastal neighborhood along the Bosphorus in Istanbul, known for its scenic waterfront and residential character.
  • 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_69ca8285d6488190a95d4c02d7354b53 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb14c589748190b34d0911d373e194 completed March 31, 2026, 12:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbdf0394348190b5928ffb9e3df45e completed March 31, 2026, 2:49 p.m.
NEDg Description generation batch_69cbe436e20481908b297cd94eafbeec completed March 31, 2026, 3:11 p.m.
NED2 Entity disambiguation (via description) batch_69cc0c32aac081909cdd0d69cacdd27f completed March 31, 2026, 6:02 p.m.
Created at: March 30, 2026, 4:47 p.m.