Triple

T4495911
Position Surface form Disambiguated ID Type / Status
Subject Seubelsdorf E100694 entity
Predicate partOf P40 FINISHED
Object town of Lichtenfels E11679 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: town of Lichtenfels | Statement: [Seubelsdorf, partOf, town of Lichtenfels]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: town of Lichtenfels
Context triple: [Seubelsdorf, partOf, town of Lichtenfels]
  • A. Lichtenfels chosen
    Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
  • B. Lichtenfels district
    Lichtenfels district is a rural administrative district in the Bavarian region of Upper Franconia in Germany, known for its historic towns and traditional basket-weaving industry.
  • C. Lampoldshausen
    Lampoldshausen is a German village best known as a major site for rocket propulsion research and testing facilities of the German Aerospace Center.
  • D. County of Erbach
    The County of Erbach was a small territorial state of the Holy Roman Empire in what is now southwestern Germany, historically ruled by the Counts of Erbach.
  • E. Taufkirchen
    Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
  • 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_69bd43cdf15081909a4fa2585ff63b3e completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd56bde14c819091d42839a46291d0 completed March 20, 2026, 2:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69bd67bfb4788190b64975b1999a8d1e completed March 20, 2026, 3:29 p.m.
Created at: March 20, 2026, 1 p.m.