Triple

T31262248

Position	Surface form	Disambiguated ID	Type / Status
Subject	Barbara Lillian Combes	`E797152`	entity
Predicate	participantIn	`P149`	FINISHED
Object	The New Gidget The New Gidget is a 1980s American sitcom that continues the adventures of the surfing teen character Gidget as an adult, originally popularized in 1960s films and television.	`E1954092`	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: The New Gidget | Statement: [Barbara Lillian Combes, participantIn, The New Gidget]

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: The New Gidget
Triple: [Barbara Lillian Combes, participantIn, The New Gidget]

Generated description

The New Gidget is a 1980s American sitcom that continues the adventures of the surfing teen character Gidget as an adult, originally popularized in 1960s films and television.

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_69f224dd5fdc81908a4cd24917b67668`	completed	April 29, 2026, 3:33 p.m.
NER	Named-entity recognition	`batch_69f69d8de8448190b337feaed4883acc`	completed	May 3, 2026, 12:57 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a296bfc462c8190a62caec416fb8fb3`	completed	June 10, 2026, 1:51 p.m.
NEDg	Description generation	`batch_6a296fe7a5848190bb96205a6ede9dc2`	completed	June 10, 2026, 2:08 p.m.
NED2	Entity disambiguation (via description)	`batch_6a29a7f163ac819080e504a3bd158340`	completed	June 10, 2026, 6:07 p.m.

Created at: April 29, 2026, 9:12 p.m.