Triple

T37200347
Position Surface form Disambiguated ID Type / Status
Subject Jim Baker (Sixteen Candles) E921714 entity
Predicate spouse P13 FINISHED
Object Brenda Baker (Sixteen Candles)
Brenda Baker is the well-meaning but often distracted mother of Samantha and Ginny in the 1984 teen comedy film "Sixteen Candles."
E2217842 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: Brenda Baker (Sixteen Candles) | Statement: [Jim Baker (Sixteen Candles), spouse, Brenda Baker (Sixteen Candles)]
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: Brenda Baker (Sixteen Candles)
Triple: [Jim Baker (Sixteen Candles), spouse, Brenda Baker (Sixteen Candles)]
Generated description
Brenda Baker is the well-meaning but often distracted mother of Samantha and Ginny in the 1984 teen comedy film "Sixteen Candles."

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_69f76ea313a08190a54404cd1e47da90 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb36446c1881909ac9f782a56f88e2 completed May 6, 2026, 12:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40361b5c6c8190b9c51b1690f5d5e1 completed June 27, 2026, 8:44 p.m.
NEDg Description generation batch_6a4037adc8108190bf35bf75d60ee259 completed June 27, 2026, 8:50 p.m.
NED2 Entity disambiguation (via description) batch_6a403905066c8190997af99b48b21a75 completed June 27, 2026, 8:56 p.m.
Created at: May 3, 2026, 4:15 p.m.