Triple

T34280281
Position Surface form Disambiguated ID Type / Status
Subject Tower 185 E879572 entity
Predicate architect P184 FINISHED
Object Prof. Christoph Mäckler Architekten
Prof. Christoph Mäckler Architekten is a German architecture firm led by Christoph Mäckler, known for its high-rise and urban design projects such as major office towers in Frankfurt.
E2090004 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: Prof. Christoph Mäckler Architekten | Statement: [Tower 185, architect, Prof. Christoph Mäckler Architekten]
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: Prof. Christoph Mäckler Architekten
Triple: [Tower 185, architect, Prof. Christoph Mäckler Architekten]
Generated description
Prof. Christoph Mäckler Architekten is a German architecture firm led by Christoph Mäckler, known for its high-rise and urban design projects such as major office towers in Frankfurt.

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_69f349b5f6648190b9420d94a4cd16e0 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f712ee4d1081909fae80a3a2c7d32c completed May 3, 2026, 9:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36e62c3c988190befdd735a7ae7ccc completed June 20, 2026, 7:12 p.m.
NEDg Description generation batch_6a36e7e4bbc481908bf9a10010e3ed64 completed June 20, 2026, 7:20 p.m.
NED2 Entity disambiguation (via description) batch_6a36e86047e4819087ef8f50373c412d completed June 20, 2026, 7:22 p.m.
Created at: May 1, 2026, 1:57 a.m.