Triple

T6708794
Position Surface form Disambiguated ID Type / Status
Subject Lake Orta E153075 entity
Predicate hasTownOnShore P969 FINISHED
Object Omegna
Omegna is a town in northern Italy’s Piedmont region, known for its lakeside setting, industrial history, and position at the northern tip of Lake Orta.
 E613774 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: Omegna | Statement: [Lake Orta, hasTownOnShore, Omegna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Omegna
Context triple: [Lake Orta, hasTownOnShore, Omegna]
  • A. Dorla
    Dorla are an indigenous Adivasi community of the Bastar region in central India, known for their distinct cultural traditions, language, and close relationship with forest-based livelihoods.
  • B. Cosia
    Cosia is a small river in northern Italy that flows through the city of Como before emptying into Lake Como.
  • C. Kroraina
    Kroraina was an ancient Central Asian oasis kingdom in the Tarim Basin, known from Chinese records as Shanshan and important along the Silk Road.
  • D. Lugana
    Lugana is an Italian white wine appellation near Lake Garda, renowned for its fresh, mineral-driven wines primarily made from the Turbiana grape.
  • E. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • 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: Omegna
Triple: [Lake Orta, hasTownOnShore, Omegna]
Generated description
Omegna is a town in northern Italy’s Piedmont region, known for its lakeside setting, industrial history, and position at the northern tip of Lake Orta.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Omegna
Target entity description: Omegna is a town in northern Italy’s Piedmont region, known for its lakeside setting, industrial history, and position at the northern tip of Lake Orta.
  • A. Dorla
    Dorla are an indigenous Adivasi community of the Bastar region in central India, known for their distinct cultural traditions, language, and close relationship with forest-based livelihoods.
  • B. Cosia
    Cosia is a small river in northern Italy that flows through the city of Como before emptying into Lake Como.
  • C. Kroraina
    Kroraina was an ancient Central Asian oasis kingdom in the Tarim Basin, known from Chinese records as Shanshan and important along the Silk Road.
  • D. Lugana
    Lugana is an Italian white wine appellation near Lake Garda, renowned for its fresh, mineral-driven wines primarily made from the Turbiana grape.
  • E. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • 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_69c68808d8d8819087369015270788fe completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d1049b7c8190a970a165d15b440b completed March 27, 2026, 6:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7008e6b308190a3d5db2bf4a469c4 completed March 27, 2026, 10:11 p.m.
NEDg Description generation batch_69c701be78cc8190a0848ea60908d129 completed March 27, 2026, 10:16 p.m.
NED2 Entity disambiguation (via description) batch_69c7021b27288190866aef500198479d completed March 27, 2026, 10:18 p.m.
Created at: March 27, 2026, 2:06 p.m.