Triple

T24633326

Position	Surface form	Disambiguated ID	Type / Status
Subject	Sara Gama	`E609736`	entity
Predicate	formerMemberOfSportsTeam	`P8967`	FINISHED
Object	Brescia Calcio Femminile Brescia Calcio Femminile is an Italian women's football club that has been one of the country’s leading sides, competing at the top level of the women’s game in Italy.	`E1648493`	NE FINISHED

How this triple was built (3 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: Brescia Calcio Femminile | Statement: [Sara Gama, formerMemberOfSportsTeam, Brescia Calcio Femminile]

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: Brescia Calcio Femminile
Triple: [Sara Gama, formerMemberOfSportsTeam, Brescia Calcio Femminile]

Generated description

Brescia Calcio Femminile is an Italian women's football club that has been one of the country’s leading sides, competing at the top level of the women’s game in Italy.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: formerMemberOfSportsTeam
Context triple: [Sara Gama, formerMemberOfSportsTeam, Brescia Calcio Femminile]

A. formerSportsTeam chosen
Indicates that an entity was once a member of or played for a particular sports team in the past but no longer does so.
B. formerLeagueTeam
Indicates that an entity was previously a member team of a particular sports league but is no longer part of that league.
C. roleAtSportsTeam
Indicates the specific position or function an individual holds within a sports team.
D. hasFormerTeam
Indicates that an entity was previously a member of, played for, or was affiliated with a team in the past but is no longer associated with that team.
E. formerMemberOf
Indicates that an entity once belonged to or was affiliated with a group, organization, or body, but is no longer a member.
F. None of above.

Provenance (6 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_69e2c4d28f848190ac38c400060e943d`	completed	April 17, 2026, 11:40 p.m.
NER	Named-entity recognition	`batch_69f2be064ff88190b5d9e5ec75a41242`	completed	April 30, 2026, 2:27 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a100fef08a88190b88f5f3e67ff273c`	completed	May 22, 2026, 8:12 a.m.
NEDg	Description generation	`batch_6a10136992b481909ee04d5c09867f21`	completed	May 22, 2026, 8:27 a.m.
NED2	Entity disambiguation (via description)	`batch_6a10140b2fec8190aa6d805f54926b56`	completed	May 22, 2026, 8:30 a.m.
PD	Predicate disambiguation	`batch_69f2a6d0ab708190b2e3b94dd20ca76b`	completed	April 30, 2026, 12:48 a.m.

Created at: April 18, 2026, 2:32 a.m.