Triple

T18442477
Position Surface form Disambiguated ID Type / Status
Subject Fosnes E450565 entity
Predicate locatedInAdministrativeTerritorialEntity P40 FINISHED
Object Nord-Trøndelag
Nord-Trøndelag was a former county in central Norway known for its rural landscapes, coastal communities, and role as part of the historic Trøndelag region.
E1815752 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: Nord-Trøndelag | Statement: [Fosnes, locatedInAdministrativeTerritorialEntity, Nord-Trøndelag]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nord-Trøndelag
Triple: [Fosnes, locatedInAdministrativeTerritorialEntity, Nord-Trøndelag]
Generated description
Nord-Trøndelag was a former county in central Norway known for its rural landscapes, coastal communities, and role as part of the historic Trøndelag region.

Provenance (5 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_69d8d381d6388190a9e94e9c658174e4 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e51c11b1288190b9ed4497751197d1 completed April 19, 2026, 6:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1632ce645881908eb118bc619c39ae completed May 26, 2026, 11:54 p.m.
NEDg Description generation batch_6a163388462481909f4ea41cb85696b0 completed May 26, 2026, 11:58 p.m.
NED2 Entity disambiguation (via description) batch_6a1633fc869c8190b6fe8595859de273 completed May 26, 2026, 11:59 p.m.
Created at: April 10, 2026, 11:30 a.m.