Triple

T24489634
Position Surface form Disambiguated ID Type / Status
Subject Italy Fed Cup team E617605 entity
Predicate notablePlayer P304 FINISHED
Object Silvia Farina Elia
Silvia Farina Elia is a retired Italian professional tennis player known for her success on the WTA Tour and key contributions to Italy’s national team in international competitions.
E1732195 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: Silvia Farina Elia | Statement: [Italy Fed Cup team, notablePlayer, Silvia Farina Elia]
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: Silvia Farina Elia
Triple: [Italy Fed Cup team, notablePlayer, Silvia Farina Elia]
Generated description
Silvia Farina Elia is a retired Italian professional tennis player known for her success on the WTA Tour and key contributions to Italy’s national team in international competitions.

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_69e2d7f4e6bc8190aec540ae3b9ed7f2 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f2a6deb3588190b92cd8aab6d2a89a completed April 30, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a11c7dae98081909dceb23f9dbe9f22 completed May 23, 2026, 3:29 p.m.
NEDg Description generation batch_6a11ca4e5a58819081ded261719245c6 completed May 23, 2026, 3:39 p.m.
NED2 Entity disambiguation (via description) batch_6a11cac2048c81908007d7be9e205599 completed May 23, 2026, 3:41 p.m.
Created at: April 18, 2026, 2:22 a.m.