Triple

T5244478
Position Surface form Disambiguated ID Type / Status
Subject TSV 1860 Munich E118423 entity
Predicate famousPlayer P9730 FINISHED
Object Thomas Häßler
Thomas Häßler is a former German attacking midfielder renowned for his playmaking skills and key role in Germany’s 1990 World Cup and Euro 1996 triumphs.
E516618 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: Thomas Häßler | Statement: [TSV 1860 Munich, famousPlayer, Thomas Häßler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thomas Häßler
Context triple: [TSV 1860 Munich, famousPlayer, Thomas Häßler]
  • A. Otmar Hasler
    Otmar Hasler is a Liechtenstein politician who served as Prime Minister under Prince Hans-Adam II.
  • B. Andreas Bühler
    Andreas Bühler is a notable individual who shares the German surname Bühler, though specific widely recognized biographical details about him are not clearly established.
  • C. Karl Rammelt
    Karl Rammelt was a German Luftwaffe fighter ace of World War II, credited with numerous aerial victories on the Eastern Front.
  • D. Christoph Riggenbach
    Christoph Riggenbach was a Swiss architect known for designing the Kunstmuseum Basel, one of the oldest public art collections in the world.
  • E. Bernd Huber
    Bernd Huber is a German economist and academic who served as president of Ludwig Maximilian University of Munich.
  • 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: Thomas Häßler
Triple: [TSV 1860 Munich, famousPlayer, Thomas Häßler]
Generated description
Thomas Häßler is a former German attacking midfielder renowned for his playmaking skills and key role in Germany’s 1990 World Cup and Euro 1996 triumphs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Thomas Häßler
Target entity description: Thomas Häßler is a former German attacking midfielder renowned for his playmaking skills and key role in Germany’s 1990 World Cup and Euro 1996 triumphs.
  • A. Otmar Hasler
    Otmar Hasler is a Liechtenstein politician who served as Prime Minister under Prince Hans-Adam II.
  • B. Andreas Bühler
    Andreas Bühler is a notable individual who shares the German surname Bühler, though specific widely recognized biographical details about him are not clearly established.
  • C. Karl Rammelt
    Karl Rammelt was a German Luftwaffe fighter ace of World War II, credited with numerous aerial victories on the Eastern Front.
  • D. Christoph Riggenbach
    Christoph Riggenbach was a Swiss architect known for designing the Kunstmuseum Basel, one of the oldest public art collections in the world.
  • E. Bernd Huber
    Bernd Huber is a German economist and academic who served as president of Ludwig Maximilian University of Munich.
  • 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_69bd4468aacc8190a8196f71855cdf4f completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7b4fa0ec8190bce3da09aa768726 completed March 20, 2026, 4:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf331e0e2881908b52da110384302a completed March 22, 2026, 12:09 a.m.
NEDg Description generation batch_69bf33f58bc48190a3ac42d637f4a052 completed March 22, 2026, 12:12 a.m.
NED2 Entity disambiguation (via description) batch_69bf34882fdc8190acab540c5ff07bd8 completed March 22, 2026, 12:15 a.m.
Created at: March 20, 2026, 1:49 p.m.