Triple
T20063469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Essie Davis |
E499545
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Essie
Essie is an Australian actress best known for her roles in "The Babadook" and the television series "Miss Fisher's Murder Mysteries."
|
E1408345
|
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: Essie | Statement: [Essie Davis, givenName, Essie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Essie Context triple: [Essie Davis, givenName, Essie]
-
A.
Essie
Essie is a popular nail polish and nail care brand known for its wide range of fashion-forward colors and salon-quality formulas.
-
B.
Rimmel
Rimmel is a British cosmetics brand best known for its affordable makeup products and the slogan "Get the London Look."
-
C.
Revlon
Revlon is a major American cosmetics, skincare, fragrance, and personal care company known for its mass-market beauty products and global brand presence.
-
D.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
-
E.
NYX Professional Makeup
NYX Professional Makeup is a popular, affordable cosmetics brand known for its wide range of highly pigmented, trend-driven makeup products favored by both professionals and everyday consumers.
- 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: Essie Triple: [Essie Davis, givenName, Essie]
Generated description
Essie is an Australian actress best known for her roles in "The Babadook" and the television series "Miss Fisher's Murder Mysteries."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Essie Target entity description: Essie is an Australian actress best known for her roles in "The Babadook" and the television series "Miss Fisher's Murder Mysteries."
-
A.
Essie
Essie is a popular nail polish and nail care brand known for its wide range of fashion-forward colors and salon-quality formulas.
-
B.
Rimmel
Rimmel is a British cosmetics brand best known for its affordable makeup products and the slogan "Get the London Look."
-
C.
Revlon
Revlon is a major American cosmetics, skincare, fragrance, and personal care company known for its mass-market beauty products and global brand presence.
-
D.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
-
E.
NYX Professional Makeup
NYX Professional Makeup is a popular, affordable cosmetics brand known for its wide range of highly pigmented, trend-driven makeup products favored by both professionals and everyday consumers.
- 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_69da6276bcf48190aabbf279192a5fb4 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66377b6b48190a0a37279f285123e |
completed | April 20, 2026, 5:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a081612662c81909cb1e90910ca76ac |
completed | May 16, 2026, 7 a.m. |
| NEDg | Description generation | batch_6a0816d4a8e081909930e2e379a56071 |
completed | May 16, 2026, 7:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a081742ed8481908a424a3d05368e46 |
completed | May 16, 2026, 7:05 a.m. |
Created at: April 11, 2026, 3:39 p.m.