Triple

T24650099
Position Surface form Disambiguated ID Type / Status
Subject Messerschmitt KR200 E610225 entity
Predicate predecessor P97 FINISHED
Object Messerschmitt KR175
The Messerschmitt KR175 is a small three-wheeled microcar produced in the early 1950s by the German aircraft manufacturer Messerschmitt, known for its tandem seating and bubble canopy design.
E610225 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 KR175 | Statement: [Messerschmitt KR200, predecessor, Messerschmitt KR175]
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: Messerschmitt KR175
Triple: [Messerschmitt KR200, predecessor, Messerschmitt KR175]
Generated description
The Messerschmitt KR175 is a small three-wheeled microcar produced in the early 1950s by the German aircraft manufacturer Messerschmitt, known for its tandem seating and bubble canopy design.

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_69e2c4d350a481909170482bc2ce6af9 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f40f8561ac81909d38a1cd5432b305 completed May 1, 2026, 2:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101bebeaf08190939e33d7d5e46063 completed May 22, 2026, 9:03 a.m.
NEDg Description generation batch_6a10248751648190aabfa72ad8ab0b3f completed May 22, 2026, 9:40 a.m.
NED2 Entity disambiguation (via description) batch_6a10258f82b4819095231c1c9398b2c8 completed May 22, 2026, 9:44 a.m.
Created at: April 18, 2026, 2:33 a.m.