Triple

T11929179
Position Surface form Disambiguated ID Type / Status
Subject Daisen Park E283865 entity
Predicate locatedIn P40 FINISHED
Object Sakai E8407 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: Sakai | Statement: [Daisen Park, locatedIn, Sakai]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sakai
Context triple: [Daisen Park, locatedIn, Sakai]
  • A. Sakai chosen
    Sakai is a major Japanese city in Osaka Prefecture known historically as a prosperous port and merchant center and today as an important industrial and cultural hub.
  • B. Sakai Port
    Sakai Port is a historic Japanese harbor city area in Osaka Prefecture that has long served as a key commercial and maritime gateway.
  • C. Daiko Campus
    Daiko Campus is one of Nagoya University's satellite campuses in Nagoya, Japan, housing specialized faculties and research facilities.
  • D. Kindai
    Kindai is a major private university in Japan known for its comprehensive academic programs and strong research in fields such as science, engineering, and fisheries.
  • E. Sakae
    Sakae is a major downtown commercial and entertainment district in Nagoya, Japan, known for its shopping, nightlife, and landmark attractions.
  • 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_69d6ab2ce9c48190b5d39511b524f666 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90303a9a88190a4044e6310ba9b4b completed April 10, 2026, 2:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f440611e648190b2c47b43f02d2e4b completed May 1, 2026, 5:55 a.m.
Created at: April 8, 2026, 9:45 p.m.