Triple

T31958684
Position Surface form Disambiguated ID Type / Status
Subject Piazzale Donatello E815974 entity
Predicate hasNearbyStreet P8235 FINISHED
Object Viale Don Minzoni
Viale Don Minzoni is a major street in Florence, Italy, known for connecting central neighborhoods and running near historic sites such as Piazzale Donatello.
E1984047 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: Viale Don Minzoni | Statement: [Piazzale Donatello, hasNearbyStreet, Viale Don Minzoni]
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: Viale Don Minzoni
Triple: [Piazzale Donatello, hasNearbyStreet, Viale Don Minzoni]
Generated description
Viale Don Minzoni is a major street in Florence, Italy, known for connecting central neighborhoods and running near historic sites such as Piazzale Donatello.

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_69f348f4ec708190abbb2a7c3ed58844 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6b2b0d044819085a28c5114423542 completed May 3, 2026, 2:28 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2e8a51c14481909238a0b56934bd21 completed June 14, 2026, 11:02 a.m.
NEDg Description generation batch_6a2e8ad753688190829398ff32cf09e8 completed June 14, 2026, 11:04 a.m.
NED2 Entity disambiguation (via description) batch_6a2e8b9dbba88190a8e7f8aeb1d1c933 completed June 14, 2026, 11:08 a.m.
Created at: May 1, 2026, 12:08 a.m.