Triple
T14959127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lauren Hutton |
E373013
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Mary
Mary is the birth name of American model and actress Lauren Hutton, a pioneering figure in fashion known for her gap-toothed smile and influential modeling career.
|
E1128629
|
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: Mary | Statement: [Lauren Hutton, givenName, Mary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Context triple: [Lauren Hutton, givenName, Mary]
-
A.
Mary
Mary of Waltham, Duchess of Brittany, was a 14th-century English princess and daughter of King Edward III who became duchess through her marriage to John IV, Duke of Brittany.
-
B.
Mary
Mary is the middle name of Edith Tolkien, the wife of author J.R.R. Tolkien.
-
C.
Mary
Mary is the birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
-
D.
Mary
Mary is a character portrayed by actress and filmmaker Alice Englert.
-
E.
Mary
Mary is the given name of American author Mary E. Wilkins Freeman, known for her regionalist short stories and novels depicting New England village life and women’s experiences.
- 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: Mary Triple: [Lauren Hutton, givenName, Mary]
Generated description
Mary is the birth name of American model and actress Lauren Hutton, a pioneering figure in fashion known for her gap-toothed smile and influential modeling career.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Target entity description: Mary is the birth name of American model and actress Lauren Hutton, a pioneering figure in fashion known for her gap-toothed smile and influential modeling career.
-
A.
Mary
Mary is the birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
-
B.
Mary
Mary is the birth name of American actress, singer, and dancer Debbie Reynolds, a major Hollywood star of the mid-20th century.
-
C.
Mary
Mary is the birth name of the acclaimed British actress Vivien Leigh, renowned for her roles in "Gone with the Wind" and "A Streetcar Named Desire."
-
D.
Mary
Mary is the given first name of the American actress Kathleen Turner, known for her distinctive husky voice and prominent film roles in the 1980s.
-
E.
Mary
Mary is the given name of Mary Woronov, an American actress, author, and underground film icon associated with Andy Warhol’s Factory scene.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cd85bc81909040b7ff78f62554 |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e8e9c0c81909cfb1e02987527c0 |
completed | May 9, 2026, 12:23 a.m. |
| NEDg | Description generation | batch_69fe7f299cf081909a3e15ead54bd2fc |
completed | May 9, 2026, 12:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe7fb4aa5c8190bca9fc60a1ef6833 |
completed | May 9, 2026, 12:28 a.m. |
Created at: April 10, 2026, 2:40 a.m.