Triple
T9094238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paula Kent Meehan |
E217972
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Redken |
E27579
|
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: Redken | Statement: [Paula Kent Meehan, employer, Redken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Redken Context triple: [Paula Kent Meehan, employer, Redken]
-
A.
Redken
chosen
Redken is a professional haircare and hair color brand known for its salon-quality products and innovative, science-driven formulas.
-
B.
Tresemmé
Tresemmé is a popular hair care brand known for its salon-inspired shampoos, conditioners, and styling products widely available in retail stores.
-
C.
Schwarzkopf
Schwarzkopf is a German surname most prominently associated with U.S. Army General Norman Schwarzkopf Jr., who led coalition forces in the Gulf War.
-
D.
Biolage
Biolage is a professional haircare brand known for salon-quality products that emphasize botanical ingredients and sustainable practices.
-
E.
Pureology
Pureology is a professional haircare brand best known for its sulfate-free, color-protecting products used in salons and at home.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca83d8ab5881909d8fddae363b32b1 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc96b4a2e0819092f4eae8b1d21f33 |
completed | April 1, 2026, 3:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d03014ea488190abc71a0ee182bcad |
completed | April 3, 2026, 9:24 p.m. |
Created at: March 30, 2026, 7:14 p.m.