Triple

T12971622
Position Surface form Disambiguated ID Type / Status
Subject Hesse-Nassau E321410 entity
Predicate contains P35 FINISHED
Object Fulda E161070 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: Fulda | Statement: [Hesse-Nassau, contains, Fulda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fulda
Context triple: [Hesse-Nassau, contains, Fulda]
  • A. Fulda
    Fulda is a major river in central Germany that flows through the state of Hesse and joins the Werra to form the Weser.
  • B. Fulda chosen
    Fulda is a historic city in central Germany known for its Baroque architecture and former status as an important monastic and ecclesiastical center.
  • C. Merseburg
    Merseburg is a historic town in the German state of Saxony-Anhalt, known for its medieval cathedral and role as an important cultural and administrative center on the River Saale.
  • D. Hildesheim
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • E. Fritzlar
    Fritzlar is a historic town in northern Hesse, Germany, known for its well-preserved medieval old town and its significance in early German Christian history.
  • 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_69d80763bd6c819094437da5b20b01d2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e418d548190be1c73db76cb3aa8 completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6eacea664819096940ba4d409d264 completed May 3, 2026, 6:27 a.m.
Created at: April 9, 2026, 8:36 p.m.