Triple

T26739625
Position Surface form Disambiguated ID Type / Status
Subject Autostrada A23 E674209 entity
Predicate locatedIn P40 FINISHED
Object Province of Tarvisio
The Province of Tarvisio is a mountainous area in northeastern Italy near the Austrian and Slovenian borders, known for its alpine landscapes, ski resorts, and strategic transport routes through the Alps.
E1791343 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: Province of Tarvisio | Statement: [Autostrada A23, locatedIn, Province of Tarvisio]
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: Province of Tarvisio
Triple: [Autostrada A23, locatedIn, Province of Tarvisio]
Generated description
The Province of Tarvisio is a mountainous area in northeastern Italy near the Austrian and Slovenian borders, known for its alpine landscapes, ski resorts, and strategic transport routes through the Alps.

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_69eecda57ab481909424e98f2835e7d8 completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f6184770d08190b6cb20a1cc91baf0 completed May 2, 2026, 3:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12f6f8014481909c7352b6a9cd7e50 completed May 24, 2026, 1:02 p.m.
NEDg Description generation batch_6a12f79fed1c81908af492a3fd35f82d completed May 24, 2026, 1:05 p.m.
NED2 Entity disambiguation (via description) batch_6a12fbae881c8190a13234bf6ad26f8f completed May 24, 2026, 1:22 p.m.
Created at: April 27, 2026, 3:48 a.m.