Triple
T10392360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mira Nair |
E244923
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Nair
Nair is a common Indian surname and community name, particularly associated with a historically influential Hindu caste group from the state of Kerala.
|
E859966
|
NE FINISHED |
How this triple was built (4 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: Nair | Statement: [Mira Nair, familyName, Nair]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nair Context triple: [Mira Nair, familyName, Nair]
-
A.
Neutrogena Wing
Neutrogena Wing is a dedicated gallery space within the Museum of International Folk Art that showcases a significant collection of global folk and traditional arts.
-
B.
Axe
Axe is a popular men’s grooming brand known for its deodorants, body sprays, and personal care products marketed with a youthful, edgy image.
-
C.
Garnier
Garnier is a French surname most famously associated with architect Charles Garnier, designer of the Paris Opéra.
-
D.
Skoal
Skoal is a leading U.S. smokeless tobacco brand best known for its moist snuff products.
-
E.
Neutrogena
Neutrogena is a widely recognized skincare and cosmetics brand known for its dermatologist-recommended products, including facial cleansers, moisturizers, sunscreens, and acne treatments.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Nair Triple: [Mira Nair, familyName, Nair]
Generated description
Nair is a common Indian surname and community name, particularly associated with a historically influential Hindu caste group from the state of Kerala.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nair Target entity description: Nair is a common Indian surname and community name, particularly associated with a historically influential Hindu caste group from the state of Kerala.
-
A.
Neutrogena Wing
Neutrogena Wing is a dedicated gallery space within the Museum of International Folk Art that showcases a significant collection of global folk and traditional arts.
-
B.
Axe
Axe is a popular men’s grooming brand known for its deodorants, body sprays, and personal care products marketed with a youthful, edgy image.
-
C.
Garnier
Garnier is a French surname most famously associated with architect Charles Garnier, designer of the Paris Opéra.
-
D.
Skoal
Skoal is a leading U.S. smokeless tobacco brand best known for its moist snuff products.
-
E.
Neutrogena
Neutrogena is a widely recognized skincare and cosmetics brand known for its dermatologist-recommended products, including facial cleansers, moisturizers, sunscreens, and acne treatments.
- F. None of above. chosen
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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9b6c750819087678bf81a3ef806 |
completed | April 7, 2026, 11:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d795c0253c8190b6141ccb90fd1b7e |
completed | April 9, 2026, 12:04 p.m. |
| NEDg | Description generation | batch_69d799bd91e4819085fbd44d524aaf97 |
completed | April 9, 2026, 12:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d79a6086fc8190ab8454a216dfda8c |
completed | April 9, 2026, 12:24 p.m. |
Created at: April 6, 2026, 12:06 p.m.