Triple

T37231516
Position Surface form Disambiguated ID Type / Status
Subject Via D'Amelio bombing E923146 entity
Predicate location P40 FINISHED
Object Via Mariano D'Amelio
Via Mariano D'Amelio is a street in Palermo, Sicily, best known as the site of the 1992 mafia bombing that killed anti-mafia judge Paolo Borsellino and his police escort.
E2218919 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: Via Mariano D'Amelio | Statement: [Via D'Amelio bombing, location, Via Mariano D'Amelio]
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: Via Mariano D'Amelio
Triple: [Via D'Amelio bombing, location, Via Mariano D'Amelio]
Generated description
Via Mariano D'Amelio is a street in Palermo, Sicily, best known as the site of the 1992 mafia bombing that killed anti-mafia judge Paolo Borsellino and his police escort.

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_69f76ea7f0008190b31b8e30f3d05a71 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb36cb19688190adb8f56c9918e6cb completed May 6, 2026, 12:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4043c0d3288190b6541743ab7b8329 completed June 27, 2026, 9:42 p.m.
NEDg Description generation batch_6a40443a34148190b5b0848559466617 completed June 27, 2026, 9:44 p.m.
NED2 Entity disambiguation (via description) batch_6a404639e5a88190a204ac46e57a660f completed June 27, 2026, 9:52 p.m.
Created at: May 3, 2026, 4:15 p.m.