Triple

T12984071
Position Surface form Disambiguated ID Type / Status
Subject Jönköping University E321722 entity
Predicate city P40 FINISHED
Object Jönköping E82587 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: Jönköping | Statement: [Jönköping University, city, Jönköping]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jönköping
Context triple: [Jönköping University, city, Jönköping]
  • A. Jönköping chosen
    Jönköping is a city in southern Sweden, located at the southern end of Lake Vättern and known as a regional commercial and logistical hub.
  • B. Nyköping
    Nyköping is a historic coastal town in southeastern Sweden known for its medieval castle, harbor, and role as a regional administrative and cultural center.
  • C. Norrköping
    Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
  • D. Skövde
    Skövde is a town in south-central Sweden that serves as a major military hub and training center for the Swedish Army.
  • E. Växjö
    Växjö is a city in southern Sweden known for its lakeside setting, environmental sustainability initiatives, and role as a regional cultural and educational center.
  • 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_69d8076479b8819090afce3591939cdf completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e5e3f208190abd2d4b4d5114834 completed April 10, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd3237ccf88190974ff1a49c3a874b completed May 8, 2026, 12:45 a.m.
Created at: April 9, 2026, 8:39 p.m.