Triple

T29295913
Position Surface form Disambiguated ID Type / Status
Subject Max Littmann E742827 entity
Predicate notableWork P4 FINISHED
Object Staatstheater am Gärtnerplatz, Munich
Staatstheater am Gärtnerplatz in Munich is a historic state theatre renowned for its productions of opera, operetta, and musicals in an intimate, traditional setting.
E1862450 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: Staatstheater am Gärtnerplatz, Munich | Statement: [Max Littmann, notableWork, Staatstheater am Gärtnerplatz, Munich]
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: Staatstheater am Gärtnerplatz, Munich
Triple: [Max Littmann, notableWork, Staatstheater am Gärtnerplatz, Munich]
Generated description
Staatstheater am Gärtnerplatz in Munich is a historic state theatre renowned for its productions of opera, operetta, and musicals in an intimate, traditional setting.

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_69f0912323c48190b9a24ef8cf359225 completed April 28, 2026, 10:51 a.m.
NER Named-entity recognition batch_69f66543491c8190a45fb81ecd34469b completed May 2, 2026, 8:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25a85c1c988190ac954b9e1470ad08 completed June 7, 2026, 5:20 p.m.
NEDg Description generation batch_6a25b3a39d548190aa66a96e9d1911d0 completed June 7, 2026, 6:08 p.m.
NED2 Entity disambiguation (via description) batch_6a25b3f13e34819097faef168884e81c completed June 7, 2026, 6:09 p.m.
Created at: April 28, 2026, 1:06 p.m.