Triple

T9997364
Position Surface form Disambiguated ID Type / Status
Subject Mette Towley E197233 entity
Predicate hasGivenName P17 FINISHED
Object Mette
Mette is a given name most notably associated with American dancer and actress Mette Towley, known for her work in music videos and film.
 E833688 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: Mette | Statement: [Mette Towley, hasGivenName, Mette]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mette
Context triple: [Mette Towley, hasGivenName, Mette]
  • A. Jette
    Jette is a municipality in the Brussels-Capital Region of Belgium, known for its residential character and educational institutions, including the Jette campus.
  • B. Metter
    The Metter is a river in Germany that flows through the state of Baden-Württemberg and ultimately joins the Enz River.
  • C. Grenaa
    Grenaa is a coastal town in eastern Jutland, Denmark, known for its ferry connections to the island of Anholt and its role as a regional commercial and educational center.
  • D. Maribo
    Maribo is a historic market town on the Danish island of Lolland, known for its cathedral and lakeside setting.
  • E. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • 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: Mette
Triple: [Mette Towley, hasGivenName, Mette]
Generated description
Mette is a given name most notably associated with American dancer and actress Mette Towley, known for her work in music videos and film.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mette
Target entity description: Mette is a given name most notably associated with American dancer and actress Mette Towley, known for her work in music videos and film.
  • A. Jette
    Jette is a municipality in the Brussels-Capital Region of Belgium, known for its residential character and educational institutions, including the Jette campus.
  • B. Metter
    The Metter is a river in Germany that flows through the state of Baden-Württemberg and ultimately joins the Enz River.
  • C. Grenaa
    Grenaa is a coastal town in eastern Jutland, Denmark, known for its ferry connections to the island of Anholt and its role as a regional commercial and educational center.
  • D. Maribo
    Maribo is a historic market town on the Danish island of Lolland, known for its cathedral and lakeside setting.
  • E. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • 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_69ca82f3b61c81908ecc2c1c96dbc2e4 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdcc8aa1a881909879a694496f11a5 completed April 2, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69d258439fe88190b17da69f542ecf61 completed April 5, 2026, 12:40 p.m.
NEDg Description generation batch_69d259701e488190b288c9f523a1ec87 completed April 5, 2026, 12:45 p.m.
NED2 Entity disambiguation (via description) batch_69d259da25e081909ac184f4fa80c57e completed April 5, 2026, 12:47 p.m.
Created at: March 30, 2026, 8:51 p.m.