Triple

T36432358

Position	Surface form	Disambiguated ID	Type / Status
Subject	Helen Wills Moody	`E897476`	entity
Predicate	notableRival	`P893`	FINISHED
Object	Helen Jacobs Helen Jacobs was a leading American tennis player of the 1930s, known for winning multiple Grand Slam titles and for her intense rivalry with Helen Wills Moody.	`E2198564`	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: Helen Jacobs | Statement: [Helen Wills Moody, notableRival, Helen Jacobs]

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: Helen Jacobs
Triple: [Helen Wills Moody, notableRival, Helen Jacobs]

Generated description

Helen Jacobs was a leading American tennis player of the 1930s, known for winning multiple Grand Slam titles and for her intense rivalry with Helen Wills Moody.

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_69f76e56636481908eda808ab0273401`	completed	May 3, 2026, 3:48 p.m.
NER	Named-entity recognition	`batch_69f7bd663c348190848bdafb9d8373bf`	completed	May 3, 2026, 9:25 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a3d1781558c8190a386908f82d40585`	completed	June 25, 2026, 11:56 a.m.
NEDg	Description generation	`batch_6a3d1edebb8c81908fd5efffd31ee426`	completed	June 25, 2026, 12:28 p.m.
NED2	Entity disambiguation (via description)	`batch_6a3d66d6a75c8190af8772adb65e6d0b`	completed	June 25, 2026, 5:35 p.m.

Created at: May 3, 2026, 4:10 p.m.