Triple

T18500971
Position Surface form Disambiguated ID Type / Status
Subject Seeland (Bernese Lakeland) E452063 entity
Predicate containsTown P847 FINISHED
Object Ins 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: Ins | Statement: [Seeland (Bernese Lakeland), containsTown, Ins]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ins
Context triple: [Seeland (Bernese Lakeland), containsTown, Ins]
  • A. Ins chosen
    Ins is a small Swiss municipality located in the Seeland region of the canton of Bern, known for its agricultural landscape and proximity to the lakes of Biel, Neuchâtel, and Murten.
  • B. INS
    INS was the former U.S. federal agency responsible for administering and enforcing immigration and naturalization laws before its functions were transferred to the Department of Homeland Security.
  • C. INS
    INS is the acronym for Tunisia’s National Institute of Statistics, the official government body responsible for producing and disseminating national statistical data.
  • D. Inst
    Inst is the common nickname for the Royal Belfast Academical Institution, a historic and prestigious grammar school in Belfast, Northern Ireland.
  • E. It
    It is a 1986 horror novel by Stephen King about a shape-shifting entity that terrorizes children in the town of Derry, Maine.
  • 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_69d8d3855d50819097fc8561b0299dd9 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e532c43de48190b49b87c1bb591016 completed April 19, 2026, 7:53 p.m.
Created at: April 10, 2026, 11:36 a.m.