Triple

T20973955
Position Surface form Disambiguated ID Type / Status
Subject Manuel da Costa E516574 entity
Predicate playedFor P2170 FINISHED
Object Sivasspor NE NERFINISHED

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: Sivasspor | Statement: [Manuel da Costa, playedFor, Sivasspor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sivasspor
Context triple: [Manuel da Costa, playedFor, Sivasspor]
  • A. Sivasspor chosen
    Sivasspor is a professional Turkish football club based in Sivas that competes in the country's top leagues and has featured notable international players.
  • B. Sakaryaspor
    Sakaryaspor is a Turkish professional football club known for developing notable talents such as legendary striker Hakan Şükür.
  • C. Tuzlaspor
    Tuzlaspor is a Turkish professional football club based in the Tuzla district of Istanbul that competes in the country's league system.
  • D. Giresunspor
    Giresunspor is a Turkish professional football club based in the city of Giresun that competes in the national league system.
  • E. Manisaspor
    Manisaspor is a Turkish professional football club based in the city of Manisa, known for competing in the national league system and developing local talent.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4fee5ac8190875fa9ceba1a5e5e completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6fba2406c8190bd75dec585c14bfa completed April 21, 2026, 4:22 a.m.
Created at: April 16, 2026, 1:45 p.m.