Triple

T8591882
Position Surface form Disambiguated ID Type / Status
Subject Laitila E203445 entity
Predicate hasNeighboringMunicipality P224 FINISHED
Object Eura
Eura is a municipality in southwestern Finland known for its rich archaeological heritage and prehistoric sites.
 E765916 NE FINISHED

How this triple was built (4 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: Eura | Statement: [Laitila, hasNeighboringMunicipality, Eura]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Eura
Context triple: [Laitila, hasNeighboringMunicipality, Eura]
  • A. Evra
    Evra is a French former professional footballer best known for his successful career as a left-back with Manchester United and the French national team.
  • B. Europos
    Europos is an ancient city historically known as Rayy (or Rey), located near modern-day Tehran in Iran and recognized as one of the oldest continuously inhabited settlements in the region.
  • C. Europos
    Europos was an ancient Macedonian town traditionally identified as the birthplace of the Seleucid Empire’s founder, Seleucus I Nicator.
  • D. Europaeum
    Europaeum is a network of leading European universities dedicated to promoting academic collaboration, European studies, and cross-border dialogue in higher education.
  • E. Europol
    Europol is the European Union’s law enforcement agency that supports member states in combating serious international crime and terrorism.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Eura
Triple: [Laitila, hasNeighboringMunicipality, Eura]
Generated description
Eura is a municipality in southwestern Finland known for its rich archaeological heritage and prehistoric sites.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Eura
Target entity description: Eura is a municipality in southwestern Finland known for its rich archaeological heritage and prehistoric sites.
  • A. Evra
    Evra is a French former professional footballer best known for his successful career as a left-back with Manchester United and the French national team.
  • B. Europos
    Europos is an ancient city historically known as Rayy (or Rey), located near modern-day Tehran in Iran and recognized as one of the oldest continuously inhabited settlements in the region.
  • C. Europos
    Europos was an ancient Macedonian town traditionally identified as the birthplace of the Seleucid Empire’s founder, Seleucus I Nicator.
  • D. Europaeum
    Europaeum is a network of leading European universities dedicated to promoting academic collaboration, European studies, and cross-border dialogue in higher education.
  • E. Europol
    Europol is the European Union’s law enforcement agency that supports member states in combating serious international crime and terrorism.
  • F. None of above. chosen

Provenance (5 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_69ca832a7f108190b4e4f5648abf4aa2 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc466747b88190b752f78f361140cb completed March 31, 2026, 10:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69cfb9f4e2d881908fb539e842c20e8d completed April 3, 2026, 1 p.m.
NEDg Description generation batch_69cfba99abd88190bd3ec4ea1400f23d completed April 3, 2026, 1:03 p.m.
NED2 Entity disambiguation (via description) batch_69cfbb86d43c81909090169dfb2f31fb completed April 3, 2026, 1:07 p.m.
Created at: March 30, 2026, 6:23 p.m.