Triple

T37316893
Position Surface form Disambiguated ID Type / Status
Subject Anna McNeill Whistler E926360 entity
Predicate birthName P65 FINISHED
Object Anna McNeill
Anna McNeill was a 19th-century American woman best known as the mother and frequent model of painter James McNeill Whistler, most famously depicted in his work "Whistler's Mother."
E2243562 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: Anna McNeill | Statement: [Anna McNeill Whistler, birthName, Anna McNeill]
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: Anna McNeill
Triple: [Anna McNeill Whistler, birthName, Anna McNeill]
Generated description
Anna McNeill was a 19th-century American woman best known as the mother and frequent model of painter James McNeill Whistler, most famously depicted in his work "Whistler's Mother."

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_69f76eb28af88190b093b32e3fd614ab completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb5b3c412c819082fd88af6e1af4fc completed May 6, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40f1638290819099a14dabd62a7ef5 completed June 28, 2026, 10:03 a.m.
NEDg Description generation batch_6a40f2540f848190b7ac57b130d8234d completed June 28, 2026, 10:07 a.m.
NED2 Entity disambiguation (via description) batch_6a40f2dd54488190b0da6cb656706f54 completed June 28, 2026, 10:09 a.m.
Created at: May 3, 2026, 4:16 p.m.