Triple

T12877822
Position Surface form Disambiguated ID Type / Status
Subject Leipzig metropolitan region E308012 entity
Predicate containsCity P294 FINISHED
Object Leißling E795291 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: Leißling | Statement: [Leipzig metropolitan region, containsCity, Leißling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leißling
Context triple: [Leipzig metropolitan region, containsCity, Leißling]
  • A. Leißling chosen
    Leißling is a small municipality in the Weißenfels area of Saxony-Anhalt, Germany, known for its local industry and residential character.
  • B. Schönbichl
    Schönbichl is a small island located in the Eibsee, a picturesque alpine lake at the foot of Germany’s Zugspitze mountain.
  • C. Neulengbach
    Neulengbach is a small town in Lower Austria known for its historic center and its location within the Vienna Woods region.
  • D. Raichberg
    Raichberg is a prominent mountain in southwestern Germany known for its scenic views and hiking opportunities in the Swabian Jura region.
  • E. Mürz
    Mürz is a river in southeastern Austria that flows through the state of Styria and is an important tributary of the Mur River.
  • 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_69d7bdf69bc48190af6c2621f28ca351 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d970fa8474819086a8af3c90f3ca84 completed April 10, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6eaca8958819086df70db2ba497a5 completed May 3, 2026, 6:27 a.m.
Created at: April 9, 2026, 5:38 p.m.