Triple

T35090535
Position Surface form Disambiguated ID Type / Status
Subject Nina Mae McKinney E1012707 entity
Predicate alsoKnownAs P39 FINISHED
Object The Black Garbo
The Black Garbo was the nickname of Nina Mae McKinney, a pioneering African American film and stage actress and singer celebrated for her beauty, charisma, and barrier-breaking roles during the early 20th century.
 E2125334 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 Black Garbo | Statement: [Nina Mae McKinney, alsoKnownAs, The Black Garbo]
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 Black Garbo
Triple: [Nina Mae McKinney, alsoKnownAs, The Black Garbo]
Generated description
The Black Garbo was the nickname of Nina Mae McKinney, a pioneering African American film and stage actress and singer celebrated for her beauty, charisma, and barrier-breaking roles during the early 20th century.

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_69f76dd432ec8190969bc32acfc152b1 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78bdc662081909928c6a449e6c134 completed May 3, 2026, 5:54 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37cfeb8af88190bfc1aa9aa6d8f910 completed June 21, 2026, 11:50 a.m.
NEDg Description generation batch_6a37d0a8cc748190989640faa3a1c600 completed June 21, 2026, 11:53 a.m.
NED2 Entity disambiguation (via description) batch_6a37d140ec9481909f08dbd8c40d1ec7 completed June 21, 2026, 11:55 a.m.
Created at: May 3, 2026, 4:01 p.m.