Triple
T15971351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huda Beauty |
E387330
|
entity |
| Predicate | hasKeyPerson |
P256
|
FINISHED |
| Object | Mona Kattan |
E1186348
|
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: Mona Kattan | Statement: [Huda Beauty, hasKeyPerson, Mona Kattan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mona Kattan Context triple: [Huda Beauty, hasKeyPerson, Mona Kattan]
-
A.
Mona Kattan
chosen
Mona Kattan is a beauty entrepreneur and influencer best known for co-founding the global cosmetics brand Huda Beauty alongside her sister Huda Kattan.
-
B.
Dina Dalal
Dina Dalal is a fiercely independent, middle-aged Parsi widow in Mumbai whose struggle to maintain autonomy amid political turmoil and social injustice forms the emotional core of Rohinton Mistry’s novel *A Fine Balance*.
-
C.
Nayla Kassis
Nayla Kassis is a person bearing the surname Kassis, noted as a distinct individual associated with that family name.
-
D.
Mona Qureshi
Mona Qureshi is a television producer known for her work on high-profile British drama series, including the 2018 adaptation of Les Misérables.
-
E.
Anna Kashfi
Anna Kashfi was a British-Indian actress and the first wife of Hollywood star Marlon Brando.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15729d73c8190a4140a0e55ee2566 |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf1893388190800f013fab415ae7 |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:54 a.m.