Triple

T5012812
Position Surface form Disambiguated ID Type / Status
Subject Werra E112665 entity
Predicate passesNear P416 FINISHED
Object Eisenach E153084 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: Eisenach | Statement: [Werra, passesNear, Eisenach]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eisenach
Context triple: [Werra, passesNear, Eisenach]
  • A. Eisenach chosen
    Eisenach is a historic town in central Germany best known for its associations with Martin Luther and as the birthplace of composer Johann Sebastian Bach.
  • B. Kronach
    Kronach is a historic town in northern Bavaria, Germany, known for its well-preserved medieval old town and the imposing Rosenberg Fortress.
  • C. Gotha
    Gotha is a historic German city in Thuringia known for its former ducal court, cultural heritage, and role as a residence of various German noble houses.
  • D. Mühlhausen
    Mühlhausen is a historic town in central Germany, known for its well-preserved medieval architecture and cultural heritage.
  • E. Schmalkalden
    Schmalkalden is a historic town in the German state of Thuringia, known for its well-preserved medieval architecture and role in Reformation-era politics.
  • 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_69bd4434acb8819086679dbeccc2fe54 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd730f12a481908a27c15dc73987c6 completed March 20, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69bec34c4b8c8190a91be145e105f129 completed March 21, 2026, 4:11 p.m.
Created at: March 20, 2026, 1:35 p.m.