Triple

T11830766
Position Surface form Disambiguated ID Type / Status
Subject Fort San Sebastian E281383 entity
Predicate locatedIn P40 FINISHED
Object Shama E577145 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: Shama | Statement: [Fort San Sebastian, locatedIn, Shama]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shama
Context triple: [Fort San Sebastian, locatedIn, Shama]
  • A. Shama chosen
    Shama is a coastal town in Ghana known historically as a fishing community and trading post along the Gulf of Guinea.
  • B. Shamiya
    Shamiya is a residential district in Kuwait City known for its planned layout, community facilities, and central location within the capital.
  • C. Waras
    Waras is a significant town in Afghanistan’s central highland region of Hazarajat, serving as an important local hub for the surrounding Hazara communities.
  • D. Seppa
    Seppa is a town in the East Kameng district of Arunachal Pradesh in northeastern India, serving as an administrative and cultural center in the Himalayan foothills.
  • E. Shimsha
    Shimsha is a river in southern India that flows through Karnataka and is known for its waterfalls and contribution to the Kaveri river system.
  • 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_69d6ab276f8c8190b1966a0ef11349ac completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a62b75dc8190b27d24e46a262a11 completed April 10, 2026, 7:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69f16741d9a08190b6d6d5e59dfa41b8 completed April 29, 2026, 2:04 a.m.
Created at: April 8, 2026, 9:43 p.m.