Triple

T2919987
Position Surface form Disambiguated ID Type / Status
Subject Alexander Lippisch E78695 entity
Predicate employer P7 FINISHED
Object Messerschmitt E114107 NE FINISHED

How this triple was built (2 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: Messerschmitt | Statement: [Alexander Lippisch, employer, Messerschmitt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Messerschmitt
Context triple: [Alexander Lippisch, employer, Messerschmitt]
  • A. Messerschmitt chosen
    Messerschmitt was a prominent German aircraft manufacturer best known for producing fighter planes such as the Bf 109 and Me 262 during World War II.
  • B. Focke-Wulf
    Focke-Wulf was a German aircraft manufacturer best known for producing several prominent World War II military aircraft, including the Fw 190 fighter.
  • C. Heinkel
    Heinkel was a German aircraft manufacturing company best known for producing military aircraft for Nazi Germany during World War II.
  • D. Arado Flugzeugwerke
    Arado Flugzeugwerke was a German aircraft manufacturer best known for producing military planes for the Luftwaffe during World War II, including pioneering jet-powered designs.
  • E. Junkers
    Junkers was a pioneering German aircraft manufacturer renowned for producing innovative military and civilian airplanes, particularly during the early to mid-20th century.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ad8b0c2ad081909ff87050ae542bb9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad96a672f88190851e487dac18d43f completed March 8, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc632afc819098021312c748346b completed March 11, 2026, 5:23 a.m.
Created at: March 8, 2026, 2:54 p.m.