Triple

T4498112
Position Surface form Disambiguated ID Type / Status
Subject Antalya Province E100748 entity
Predicate contains P35 FINISHED
Object Kepez
Kepez is a populous district and municipality within the city of Antalya in southern Turkey, known for its residential areas and growing urban infrastructure.
 E447898 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: Kepez | Statement: [Antalya Province, contains, Kepez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kepez
Context triple: [Antalya Province, contains, Kepez]
  • A. Kidal
    Kidal is a remote desert town in northeastern Mali that serves as a key cultural and political center for Tuareg communities in the Adagh region.
  • B. Koutiala
    Koutiala is a major city in southern Mali known as an important center for cotton production and agriculture.
  • C. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • D. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • E. Kumba
    Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
  • 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: Kepez
Triple: [Antalya Province, contains, Kepez]
Generated description
Kepez is a populous district and municipality within the city of Antalya in southern Turkey, known for its residential areas and growing urban infrastructure.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kepez
Target entity description: Kepez is a populous district and municipality within the city of Antalya in southern Turkey, known for its residential areas and growing urban infrastructure.
  • A. Kidal
    Kidal is a remote desert town in northeastern Mali that serves as a key cultural and political center for Tuareg communities in the Adagh region.
  • B. Koutiala
    Koutiala is a major city in southern Mali known as an important center for cotton production and agriculture.
  • C. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • D. Kumba
    Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
  • E. Kumba
    Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
  • 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_69bd43cdf15081909a4fa2585ff63b3e completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd56c065e88190934eb0b1632d79bb completed March 20, 2026, 2:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69bd6f850824819092e518e1bd950f80 completed March 20, 2026, 4:02 p.m.
NEDg Description generation batch_69bd70324c408190abaf669c943e91e4 completed March 20, 2026, 4:05 p.m.
NED2 Entity disambiguation (via description) batch_69bd70a49cf48190b940051c7b4dd1d7 completed March 20, 2026, 4:07 p.m.
Created at: March 20, 2026, 1 p.m.