Triple

T8313042
Position Surface form Disambiguated ID Type / Status
Subject Germania Superior E194636 entity
Predicate majorCity P316 FINISHED
Object Vesontio
Vesontio was the ancient Roman city that later became Besançon, an important urban and military center in the province of Germania Superior.
 E725492 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: Vesontio | Statement: [Germania Superior, majorCity, Vesontio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vesontio
Context triple: [Germania Superior, majorCity, Vesontio]
  • A. Vehmaa
    Vehmaa is a small rural municipality in southwestern Finland known for its granite quarries and traditional countryside landscape.
  • B. Viimsi
    Viimsi is a rapidly developing suburban municipality in northern Estonia, located just northeast of the capital city Tallinn.
  • C. Summajärvi
    Summajärvi is a lake on the Karelian Isthmus in present-day Russia, historically associated with the nearby village of Summa and the Winter War battles fought in the area.
  • D. Vihti
    Vihti is a municipality in southern Finland located within the Uusimaa region, known for its lakes, rural landscapes, and proximity to the Helsinki metropolitan area.
  • E. Rautjärvi
    Rautjärvi is a municipality in South Karelia, southeastern Finland, known for its rural landscapes, lakes, and proximity to the Russian border.
  • 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: Vesontio
Triple: [Germania Superior, majorCity, Vesontio]
Generated description
Vesontio was the ancient Roman city that later became Besançon, an important urban and military center in the province of Germania Superior.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vesontio
Target entity description: Vesontio was the ancient Roman city that later became Besançon, an important urban and military center in the province of Germania Superior.
  • A. Vehmaa
    Vehmaa is a small rural municipality in southwestern Finland known for its granite quarries and traditional countryside landscape.
  • B. Viimsi
    Viimsi is a rapidly developing suburban municipality in northern Estonia, located just northeast of the capital city Tallinn.
  • C. Summajärvi
    Summajärvi is a lake on the Karelian Isthmus in present-day Russia, historically associated with the nearby village of Summa and the Winter War battles fought in the area.
  • D. Vihti
    Vihti is a municipality in southern Finland located within the Uusimaa region, known for its lakes, rural landscapes, and proximity to the Helsinki metropolitan area.
  • E. Rautjärvi
    Rautjärvi is a municipality in South Karelia, southeastern Finland, known for its rural landscapes, lakes, and proximity to the Russian border.
  • 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_69ca82e6e2648190a31eaf6f4f757b2a completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7f5173c881909f2e84d53ea33a98 completed March 31, 2026, 8:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd957ac9788190b4253cca9b4b095d completed April 1, 2026, 10 p.m.
NEDg Description generation batch_69cdab5d649c819098a7643d5a0b7827 completed April 1, 2026, 11:33 p.m.
NED2 Entity disambiguation (via description) batch_69cdb2c2e2248190bf52466abaebfe29 completed April 2, 2026, 12:05 a.m.
Created at: March 30, 2026, 5:54 p.m.