Triple

T1448881
Position Surface form Disambiguated ID Type / Status
Subject Marcus König E31240 entity
Predicate employer P7 FINISHED
Object City of Fürth E213765 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: City of Fürth | Statement: [Marcus König, employer, City of Fürth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Fürth
Context triple: [Marcus König, employer, City of Fürth]
  • A. Stadt Fürth chosen
    Stadt Fürth is a Bavarian city in Germany known for its rich Franconian cultural traditions, historic architecture, and vibrant local festivals.
  • B. Pforzheim
    Pforzheim is a city in southwestern Germany, historically known for its jewelry and watchmaking industry and its heavy destruction during World War II.
  • C. Imperial City of Friedberg
    The Imperial City of Friedberg was a self-governing Free Imperial City of the Holy Roman Empire located in what is now the German state of Hesse.
  • D. Bamberg
    Bamberg is a historic city in northern Bavaria, Germany, renowned for its well-preserved medieval old town and status as a UNESCO World Heritage Site.
  • E. Coburg
    Coburg is a historic town in northern Bavaria, Germany, known for its well-preserved medieval architecture and its former role as the seat of the Duchy of Saxe-Coburg and Gotha.
  • 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_69a499171a28819085b993a3ac78e363 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c55c408c8190917ed44d9070a2fb completed March 1, 2026, 11:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae1fb455888190a1408a25b93a70ce completed March 9, 2026, 1:17 a.m.
Created at: March 1, 2026, 8 p.m.