Triple

T3827071
Position Surface form Disambiguated ID Type / Status
Subject Lara Beach E88714 entity
Predicate hasNearbyAttraction P2064 FINISHED
Object Antalya city center E14330 NE FINISHED

How this triple was built (2 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: Antalya city center | Statement: [Lara Beach, hasNearbyAttraction, Antalya city center]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Antalya city center
Context triple: [Lara Beach, hasNearbyAttraction, Antalya city center]
  • A. Antalya chosen
    Antalya is a major resort city on Turkey’s Mediterranean coast, known for its beaches, historic old town, and role as a gateway to the Turkish Riviera.
  • B. Nazilli
    Nazilli is a town and district in Turkey’s Aydın Province, known for its agricultural production and location in the fertile Büyük Menderes River valley.
  • C. Antakya
    Antakya is a city in southern Turkey, historically known as Antioch, renowned as an important center of Hellenistic, Roman, and early Christian civilization.
  • D. Ortaköy
    Ortaköy is a lively Bosphorus-side neighborhood in Istanbul known for its waterfront mosque, cafes, and views of the Bosporus Bridge.
  • E. Fatih, Istanbul
    Fatih is a historic central district of Istanbul known for its dense concentration of Ottoman-era architecture, religious institutions, and educational establishments.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69aed9538cf881909d9ce8ca4ac7c18c completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeeb64c72c8190b5f3d376aa4ee933 completed March 9, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4fb51e6248190b242f9e498a320d3 completed March 14, 2026, 6:08 a.m.
Created at: March 9, 2026, 3:17 p.m.