Triple

T11459771
Position Surface form Disambiguated ID Type / Status
Subject Johan Åberg E271622 entity
Predicate hasFamilyName P18 FINISHED
Object Åberg
Åberg is a Swedish surname borne by various notable individuals across fields such as sports, music, and the arts.
 E926822 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: Åberg | Statement: [Johan Åberg, hasFamilyName, Åberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Åberg
Context triple: [Johan Åberg, hasFamilyName, Åberg]
  • A. Åkersberga
    Åkersberga is a suburban town in eastern Sweden that serves as the main population and service center of Österåker Municipality, northeast of Stockholm.
  • B. Åsta
    Åsta is a river in southeastern Norway that serves as a notable tributary to the country’s longest river, the Glomma.
  • C. Strömstad
    Strömstad is a coastal town and municipality in western Sweden, near the Norwegian border, known for its archipelago, tourism, and ferry connections.
  • D. Ås
    Ås is a Norwegian municipality in Viken county, south of Oslo, known for hosting the Norwegian University of Life Sciences and several national research institutes.
  • E. Arboga
    Arboga is a historic small town in central Sweden known for its medieval heritage and well-preserved old town.
  • 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: Åberg
Triple: [Johan Åberg, hasFamilyName, Åberg]
Generated description
Åberg is a Swedish surname borne by various notable individuals across fields such as sports, music, and the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Åberg
Target entity description: Åberg is a Swedish surname borne by various notable individuals across fields such as sports, music, and the arts.
  • A. Åkersberga
    Åkersberga is a suburban town in eastern Sweden that serves as the main population and service center of Österåker Municipality, northeast of Stockholm.
  • B. Åsta
    Åsta is a river in southeastern Norway that serves as a notable tributary to the country’s longest river, the Glomma.
  • C. Strömstad
    Strömstad is a coastal town and municipality in western Sweden, near the Norwegian border, known for its archipelago, tourism, and ferry connections.
  • D. Ås
    Ås is a Norwegian municipality in Viken county, south of Oslo, known for hosting the Norwegian University of Life Sciences and several national research institutes.
  • E. Arboga
    Arboga is a historic small town in central Sweden known for its medieval heritage and well-preserved old town.
  • 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_69d6aadff8888190a13f253f0d460874 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d822f2138081909408c7916cef99c9 completed April 9, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5e911c03c819081f1447b320dd2f2 completed April 20, 2026, 8:51 a.m.
NEDg Description generation batch_69e5eeb38d588190b51b6c299bb717dd completed April 20, 2026, 9:15 a.m.
NED2 Entity disambiguation (via description) batch_69e5f19dd3348190b037e09c87b528e9 completed April 20, 2026, 9:27 a.m.
Created at: April 8, 2026, 9:35 p.m.