Triple

T10429867
Position Surface form Disambiguated ID Type / Status
Subject Østlandet E245881 entity
Predicate hasMajorCity P316 FINISHED
Object Hamar E68670 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: Hamar | Statement: [Østlandet, hasMajorCity, Hamar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hamar
Context triple: [Østlandet, hasMajorCity, Hamar]
  • A. Hamar chosen
    Hamar is a town and municipality in Innlandet county, Norway, known for its rich Viking history and as a regional cultural and administrative center.
  • B. Hemsila
    Hemsila is a river in the Norwegian municipality of Hemsedal, known for its scenic valley course and popular trout fishing.
  • C. Garmsar
    Garmsar is a city in Semnan Province of north-central Iran, known as a regional transport hub and gateway between Tehran and eastern parts of the country.
  • D. Ahlat
    Ahlat is a historic town in eastern Turkey renowned for its medieval Seljuk-era cemeteries and monuments on the northwestern shore of Lake Van.
  • E. Hurdan
    Hurdan is a traditional local dialect spoken in the remote Las Hurdes region of western Spain, reflecting its distinctive cultural and historical isolation.
  • 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_69d381bf3dc08190bf35a2643e4e8f22 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea62d6448190a7f5b785467824cf completed April 7, 2026, 11:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69d89f8a5b00819080c303bb0fc82f5a completed April 10, 2026, 6:58 a.m.
Created at: April 6, 2026, 12:13 p.m.