Triple

T3901317
Position Surface form Disambiguated ID Type / Status
Subject Adler Mannheim E90494 entity
Predicate hasAbbreviation P43 FINISHED
Object MER (historical)
MER (historical) is a former abbreviation associated with the German professional ice hockey club Adler Mannheim, used in earlier periods of the team’s history.
 E398232 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: MER (historical) | Statement: [Adler Mannheim, hasAbbreviation, MER (historical)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MER (historical)
Context triple: [Adler Mannheim, hasAbbreviation, MER (historical)]
  • A. Merkens
    Merkens is a German surname most notably associated with Olympic track cyclist Toni Merkens.
  • B. Merrill
    Merrill is the wealth management and brokerage division of Bank of America, offering investment advice, financial planning, and related services to individual and institutional clients.
  • C. Merrill
    Merrill is a surname most notably associated with American actor Gary Merrill, known for his work in mid-20th-century film and television.
  • D. MRC
    MRC is an American independent film and television studio known for producing and financing a wide range of acclaimed movies and TV series.
  • E. MRC
    MRC is a major UK organization that funds and supports medical research to improve human health.
  • 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: MER (historical)
Triple: [Adler Mannheim, hasAbbreviation, MER (historical)]
Generated description
MER (historical) is a former abbreviation associated with the German professional ice hockey club Adler Mannheim, used in earlier periods of the team’s history.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MER (historical)
Target entity description: MER (historical) is a former abbreviation associated with the German professional ice hockey club Adler Mannheim, used in earlier periods of the team’s history.
  • A. Merkens
    Merkens is a German surname most notably associated with Olympic track cyclist Toni Merkens.
  • B. Merrill
    Merrill is the wealth management and brokerage division of Bank of America, offering investment advice, financial planning, and related services to individual and institutional clients.
  • C. Merrill
    Merrill is a surname most notably associated with American actor Gary Merrill, known for his work in mid-20th-century film and television.
  • D. MRC
    MRC is an American independent film and television studio known for producing and financing a wide range of acclaimed movies and TV series.
  • E. MRC
    MRC is a major UK organization that funds and supports medical research to improve human health.
  • 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_69aed95d315881908cbf1bf4a7215fbf completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeecf2f230819099abc109a0b7d916 completed March 9, 2026, 3:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51ca7636081908f98c4e22617f808 completed March 14, 2026, 8:30 a.m.
NEDg Description generation batch_69b5207c0cfc8190aae16e8a88348679 completed March 14, 2026, 8:46 a.m.
NED2 Entity disambiguation (via description) batch_69b52163bf888190b38f87d22ecd200e completed March 14, 2026, 8:50 a.m.
Created at: March 9, 2026, 3:21 p.m.