Triple

T31182987

Position	Surface form	Disambiguated ID	Type / Status
Subject	Switzerland at the Olympic Games	`E794950`	entity
Predicate	hasAthlete	`P17934`	FINISHED
Object	Belinda Bencic Belinda Bencic is a Swiss professional tennis player known for her powerful baseline game and for winning the women's singles gold medal at the Tokyo 2020 Olympic Games.	`E1948680`	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: Belinda Bencic | Statement: [Switzerland at the Olympic Games, hasAthlete, Belinda Bencic]

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: Belinda Bencic
Triple: [Switzerland at the Olympic Games, hasAthlete, Belinda Bencic]

Generated description

Belinda Bencic is a Swiss professional tennis player known for her powerful baseline game and for winning the women's singles gold medal at the Tokyo 2020 Olympic Games.

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_69f224d675d08190957198068e440422`	completed	April 29, 2026, 3:33 p.m.
NER	Named-entity recognition	`batch_69f6990ee738819092fd2956c5e6473f`	completed	May 3, 2026, 12:38 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a29473a678c819089511868af8678ac`	completed	June 10, 2026, 11:15 a.m.
NEDg	Description generation	`batch_6a2948a5987481909ef8e541053b18fa`	completed	June 10, 2026, 11:21 a.m.
NED2	Entity disambiguation (via description)	`batch_6a294935afd08190b92fef7a63346d4e`	completed	June 10, 2026, 11:23 a.m.

Created at: April 29, 2026, 9:08 p.m.