Triple

T24771853
Position Surface form Disambiguated ID Type / Status
Subject Beltrami E619745 entity
Predicate hasNotableBearer P458 FINISHED
Object Filippo Beltrami
Filippo Beltrami was an Italian army officer and partisan commander who became a symbol of the Resistance after being killed in combat against German and Fascist forces during World War II.
E2117158 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: Filippo Beltrami | Statement: [Beltrami, hasNotableBearer, Filippo Beltrami]
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: Filippo Beltrami
Triple: [Beltrami, hasNotableBearer, Filippo Beltrami]
Generated description
Filippo Beltrami was an Italian army officer and partisan commander who became a symbol of the Resistance after being killed in combat against German and Fascist forces during World War II.

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_69e2fabd04488190a2d13c97be745a2d completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f410abf6588190ac997f02a1177c19 completed May 1, 2026, 2:32 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3786b1cf408190a05ec092820cb539 completed June 21, 2026, 6:37 a.m.
NEDg Description generation batch_6a378f7c14f881908b059b59ec6c892b completed June 21, 2026, 7:15 a.m.
NED2 Entity disambiguation (via description) batch_6a37900b238c8190bda9ac2ff1af848e completed June 21, 2026, 7:17 a.m.
Created at: April 18, 2026, 4:31 a.m.