Triple

T15493762
Position Surface form Disambiguated ID Type / Status
Subject Haidhausen E378761 entity
Predicate partOf P40 FINISHED
Object Borough Au-Haidhausen E378761 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: Borough Au-Haidhausen | Statement: [Haidhausen, partOf, Borough Au-Haidhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Borough Au-Haidhausen
Context triple: [Haidhausen, partOf, Borough Au-Haidhausen]
  • A. Haidhausen area chosen
    The Haidhausen area is a historic and now trendy district of Munich known for its charming old buildings, lively cafés, and cultural venues along the Isar River.
  • B. Schöneberg district
    Schöneberg district is a central borough of Berlin, Germany, known for its vibrant cultural scene, historical significance, and diverse urban neighborhoods.
  • C. Tiergarten district
    Tiergarten district is a central Berlin borough known for its expansive Tiergarten park, government buildings, and cultural landmarks.
  • D. Bogenhausen district
    Bogenhausen district is an upscale residential and cultural area in Munich known for its historic villas, embassies, and prominent boulevards.
  • E. Dorotheenstadt
    Dorotheenstadt is a historic district in central Berlin, Germany, known for its cultural significance and notable institutions.
  • 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_69d85cd53a7c819080f5b9042c4c199e completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03fad723481908d2aa33e8f065f2f completed April 16, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3660fc6c81908caf1729260a8338 completed May 9, 2026, 1:28 p.m.
Created at: April 10, 2026, 3:49 a.m.