Triple

T29441071
Position Surface form Disambiguated ID Type / Status
Subject Estée Lauder E746712 entity
Predicate birthName P65 FINISHED
Object Josephine Esther Mentzer
Josephine Esther Mentzer, better known as Estée Lauder, was an American businesswoman and co-founder of the Estée Lauder cosmetics company, which became a global beauty empire.
 E1871824 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: Josephine Esther Mentzer | Statement: [Estée Lauder, birthName, Josephine Esther Mentzer]
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: Josephine Esther Mentzer
Triple: [Estée Lauder, birthName, Josephine Esther Mentzer]
Generated description
Josephine Esther Mentzer, better known as Estée Lauder, was an American businesswoman and co-founder of the Estée Lauder cosmetics company, which became a global beauty empire.

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_69f0a7a180e48190ae775e40047dbcb5 completed April 28, 2026, 12:27 p.m.
NER Named-entity recognition batch_69f66b1d0fd88190973ed80b5539f597 completed May 2, 2026, 9:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a260c0c8fdc81908ee8280d3dd58b84 completed June 8, 2026, 12:25 a.m.
NEDg Description generation batch_6a2611c5b05c8190bb5237a0dafb8b4f completed June 8, 2026, 12:50 a.m.
NED2 Entity disambiguation (via description) batch_6a2615e8084c8190bf17b0d50df4d1c3 completed June 8, 2026, 1:07 a.m.
Created at: April 28, 2026, 3:22 p.m.