Triple

T30244623
Position Surface form Disambiguated ID Type / Status
Subject Technik Museum Speyer E769021 entity
Predicate ownedBy P347 FINISHED
Object Auto & Technik Museum Sinsheim e.V.
Auto & Technik Museum Sinsheim e.V. is a German museum association best known for operating major technology and transport museums featuring historic cars, aircraft, and other vehicles.
 E1905095 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: Auto & Technik Museum Sinsheim e.V. | Statement: [Technik Museum Speyer, ownedBy, Auto & Technik Museum Sinsheim e.V.]
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: Auto & Technik Museum Sinsheim e.V.
Triple: [Technik Museum Speyer, ownedBy, Auto & Technik Museum Sinsheim e.V.]
Generated description
Auto & Technik Museum Sinsheim e.V. is a German museum association best known for operating major technology and transport museums featuring historic cars, aircraft, and other vehicles.

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_69f224831dc08190b2e569b987264057 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68074f0d081909ac1a54ff5f3797d completed May 2, 2026, 10:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a276451c2988190871b4d5d0bd4dc3f completed June 9, 2026, 12:54 a.m.
NEDg Description generation batch_6a2764dcc7148190b7ba48ce073f845f completed June 9, 2026, 12:57 a.m.
NED2 Entity disambiguation (via description) batch_6a2765db45d88190817f04133b5efd75 completed June 9, 2026, 1:01 a.m.
Created at: April 29, 2026, 7:39 p.m.