Triple

T1646823
Position Surface form Disambiguated ID Type / Status
Subject Erling Haaland E35600 entity
Predicate givenName P17 FINISHED
Object Erling
Erling is a masculine given name of Scandinavian origin, commonly used in Norway and other Nordic countries.
 E185633 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: Erling | Statement: [Erling Haaland, givenName, Erling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Erling
Context triple: [Erling Haaland, givenName, Erling]
  • A. Henrik
    Henrik is the given name of the renowned Norwegian mathematician Niels Henrik Abel, known for his pioneering work in algebra and analysis.
  • B. Ivar
    Ivar is a masculine given name of Old Norse origin, traditionally used in Scandinavian countries.
  • C. Morten
    Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
  • D. Håkon
    Håkon is one of the official mascots of the 1994 Winter Olympics held in Lillehammer, Norway, depicted as a Norwegian child symbolizing the country’s heritage and Olympic spirit.
  • E. Haakon
    Haakon is a Scandinavian male given name of Old Norse origin, traditionally borne by Norwegian kings and other notable figures.
  • 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: Erling
Triple: [Erling Haaland, givenName, Erling]
Generated description
Erling is a masculine given name of Scandinavian origin, commonly used in Norway and other Nordic countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Erling
Target entity description: Erling is a masculine given name of Scandinavian origin, commonly used in Norway and other Nordic countries.
  • A. Henrik
    Henrik is the given name of the renowned Norwegian mathematician Niels Henrik Abel, known for his pioneering work in algebra and analysis.
  • B. Ivar
    Ivar is a masculine given name of Old Norse origin, traditionally used in Scandinavian countries.
  • C. Morten
    Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
  • D. Håkon
    Håkon is one of the official mascots of the 1994 Winter Olympics held in Lillehammer, Norway, depicted as a Norwegian child symbolizing the country’s heritage and Olympic spirit.
  • E. Haakon
    Haakon is a Scandinavian male given name of Old Norse origin, traditionally borne by Norwegian kings and other notable figures.
  • 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_69a8860568888190a32cd9f70acbba42 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a62b26c8190bf97bb80c228b47e completed March 5, 2026, 4:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad60a4bd5481908b46f44364c15592 completed March 8, 2026, 11:42 a.m.
NEDg Description generation batch_69ad617fea508190ae6fa86cf9ea814a completed March 8, 2026, 11:46 a.m.
NED2 Entity disambiguation (via description) batch_69ad62028a448190a0c3f0dadae6a741 completed March 8, 2026, 11:48 a.m.
Created at: March 4, 2026, 7:28 p.m.