Triple

T7651382
Position Surface form Disambiguated ID Type / Status
Subject Torsten Hägerstrand E173259 entity
Predicate familyName P18 FINISHED
Object Hägerstrand
Hägerstrand is a Swedish surname most notably associated with Torsten Hägerstrand, a pioneering geographer known for his work in time geography and spatial analysis.
 E680255 NE FINISHED

How this triple was built (4 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: Hägerstrand | Statement: [Torsten Hägerstrand, familyName, Hägerstrand]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hägerstrand
Context triple: [Torsten Hägerstrand, familyName, Hägerstrand]
  • A. Malaueg
    Malaueg is an Austronesian language spoken by the Malaueg people in the northern Philippines, particularly in the province of Cagayan.
  • B. Lindeberg
    Lindeberg is a surname most notably associated with the Finnish mathematician Jarl Waldemar Lindeberg, known for his contributions to probability theory and the central limit theorem.
  • C. Myrdal
    Myrdal is a remote mountain railway station in Norway that serves as a key junction between the Bergen Line and the scenic Flåm Line.
  • D. Hedin
    Hedin is a Swedish surname most notably associated with the explorer and geographer Sven Hedin.
  • E. Linderud
    Linderud is a residential neighborhood in Oslo, Norway, known for its apartment blocks, shopping center, and access to public transportation.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Hägerstrand
Triple: [Torsten Hägerstrand, familyName, Hägerstrand]
Generated description
Hägerstrand is a Swedish surname most notably associated with Torsten Hägerstrand, a pioneering geographer known for his work in time geography and spatial analysis.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hägerstrand
Target entity description: Hägerstrand is a Swedish surname most notably associated with Torsten Hägerstrand, a pioneering geographer known for his work in time geography and spatial analysis.
  • A. Malaueg
    Malaueg is an Austronesian language spoken by the Malaueg people in the northern Philippines, particularly in the province of Cagayan.
  • B. Lindeberg
    Lindeberg is a surname most notably associated with the Finnish mathematician Jarl Waldemar Lindeberg, known for his contributions to probability theory and the central limit theorem.
  • C. Myrdal
    Myrdal is a remote mountain railway station in Norway that serves as a key junction between the Bergen Line and the scenic Flåm Line.
  • D. Hedin
    Hedin is a Swedish surname most notably associated with the explorer and geographer Sven Hedin.
  • E. Linderud
    Linderud is a residential neighborhood in Oslo, Norway, known for its apartment blocks, shopping center, and access to public transportation.
  • F. None of above. chosen

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_69c6995473348190a4f41d110d619a18 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c70175e4b88190bc40c839a42180d4 completed March 27, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c89aeb66c081909f3a3d6385637c25 completed March 29, 2026, 3:22 a.m.
NEDg Description generation batch_69c89ed393648190a32cf9267968faf5 completed March 29, 2026, 3:38 a.m.
NED2 Entity disambiguation (via description) batch_69c89f35a7488190a6a9bc3d10bedd5a completed March 29, 2026, 3:40 a.m.
Created at: March 27, 2026, 3:58 p.m.