Triple

T4636978
Position Surface form Disambiguated ID Type / Status
Subject Reichsgau Danzig-West Prussia E101554 entity
Predicate notableCity P2813 FINISHED
Object Elbing E226489 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: Elbing | Statement: [Reichsgau Danzig-West Prussia, notableCity, Elbing]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elbing
Context triple: [Reichsgau Danzig-West Prussia, notableCity, Elbing]
  • A. Elbing chosen
    Elbing is a historic Baltic port city, now known as Elbląg in Poland, that played a notable role in medieval trade as part of the Hanseatic commercial network.
  • B. Babruysk
    Babruysk is a historic city in eastern Belarus known as a former major Jewish cultural center and regional industrial hub.
  • C. Bromberg
    Bromberg is the former German name for the city of Bydgoszcz, a major urban and industrial center in present-day north-central Poland.
  • D. Vitebsk
    Vitebsk is a historic city in northeastern Belarus known as a major cultural center and the birthplace of artist Marc Chagall.
  • E. Orsha
    Orsha is a historic city in eastern Belarus known as a regional transport hub and site of several significant battles.
  • 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_69bd43d2f1c081908cd4b7ec48ecc73d completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5a62a9e48190b0cf1cbcc51f00c0 completed March 20, 2026, 2:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69be103799108190a905c28cdf302aba completed March 21, 2026, 3:27 a.m.
Created at: March 20, 2026, 1:13 p.m.