Triple

T13633983
Position Surface form Disambiguated ID Type / Status
Subject Bishopric of Freising E325797 entity
Predicate cathedralCity P9022 FINISHED
Object Freising E375515 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: Freising | Statement: [Bishopric of Freising, cathedralCity, Freising]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Freising
Context triple: [Bishopric of Freising, cathedralCity, Freising]
  • A. Freising chosen
    Freising is a historic Bavarian town near Munich, known for its cathedral hill and one of the world’s oldest operating breweries at Weihenstephan.
  • B. Traunstein
    Traunstein is a town in southeastern Bavaria, Germany, known as a regional administrative and cultural center near the Chiemsee and the Alps.
  • C. Kaufbeuren
    Kaufbeuren is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and traditional Swabian culture.
  • D. Füssen
    Füssen is a picturesque Bavarian town in southern Germany, known for its historic old town, proximity to Neuschwanstein Castle, and scenic location near the Alps.
  • E. Eichstätt
    Eichstätt is a historic Bavarian town in southern Germany known for its baroque architecture, Catholic university, and location within the Altmühltal Nature Park.
  • 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_69d8076beddc8190a53156f5bea77f5e completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc5a490508190924ac40f1dd519d6 completed April 12, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff1330527481908d518093debc9ad1 completed May 9, 2026, 10:57 a.m.
Created at: April 9, 2026, 9:51 p.m.