Triple

T36124593
Position Surface form Disambiguated ID Type / Status
Subject Gomorrah (TV series) E1044844 entity
Predicate portrayedBy P1507 FINISHED
Object Marco D'Amore
Marco D'Amore is an Italian actor and director best known for his leading role as crime boss Ciro Di Marzio in the acclaimed television series "Gomorrah."
E2218678 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: Marco D'Amore | Statement: [Gomorrah (TV series), portrayedBy, Marco D'Amore]
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: Marco D'Amore
Triple: [Gomorrah (TV series), portrayedBy, Marco D'Amore]
Generated description
Marco D'Amore is an Italian actor and director best known for his leading role as crime boss Ciro Di Marzio in the acclaimed television series "Gomorrah."

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_69f76e356c908190abc6ca1e6a05b011 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b2f62c888190ace4ccf3e254d0ad completed May 3, 2026, 8:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40439c5ae88190a6a3c9787f1f1ffe completed June 27, 2026, 9:41 p.m.
NEDg Description generation batch_6a404499f25c81909aa809e44d76ce0d completed June 27, 2026, 9:46 p.m.
NED2 Entity disambiguation (via description) batch_6a404599f71c81909f3ba82c2ea8885c completed June 27, 2026, 9:50 p.m.
Created at: May 3, 2026, 4:08 p.m.