Triple
T7857794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weiner |
E182419
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Mara Weiner
Mara Weiner is an individual notable enough to be specifically cited as a bearer of the surname Weiner.
|
E695758
|
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: Mara Weiner | Statement: [Weiner, hasNotableBearer, Mara Weiner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mara Weiner Context triple: [Weiner, hasNotableBearer, Mara Weiner]
-
A.
Lisa Weinstein
Lisa Weinstein is a film producer best known for her work on the acclaimed 1990 romantic fantasy drama "Ghost."
-
B.
Ari Wegner
Ari Wegner is an acclaimed Australian cinematographer known for her visually striking work on films such as "The Power of the Dog."
-
C.
Suzy Weiner
Suzy Weiner is best known as the wife of legendary American Olympic swimmer Mark Spitz.
-
D.
Liza Weil
Liza Weil is an American actress best known for her roles as Paris Geller on "Gilmore Girls" and Bonnie Winterbottom on "How to Get Away with Murder."
-
E.
Mary-Ellis Bunim
Mary-Ellis Bunim was an American television producer best known as a pioneer of modern reality TV, particularly through co-creating influential series like MTV’s "The Real World."
- 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: Mara Weiner Triple: [Weiner, hasNotableBearer, Mara Weiner]
Generated description
Mara Weiner is an individual notable enough to be specifically cited as a bearer of the surname Weiner.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mara Weiner Target entity description: Mara Weiner is an individual notable enough to be specifically cited as a bearer of the surname Weiner.
-
A.
Lisa Weinstein
Lisa Weinstein is a film producer best known for her work on the acclaimed 1990 romantic fantasy drama "Ghost."
-
B.
Ari Wegner
Ari Wegner is an acclaimed Australian cinematographer known for her visually striking work on films such as "The Power of the Dog."
-
C.
Suzy Weiner
Suzy Weiner is best known as the wife of legendary American Olympic swimmer Mark Spitz.
-
D.
Liza Weil
Liza Weil is an American actress best known for her roles as Paris Geller on "Gilmore Girls" and Bonnie Winterbottom on "How to Get Away with Murder."
-
E.
Mary-Ellis Bunim
Mary-Ellis Bunim was an American television producer best known as a pioneer of modern reality TV, particularly through co-creating influential series like MTV’s "The Real World."
- 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_69ca82887fd48190975896bf38c4596b |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb1a76f8648190976b488d0d8658ef |
completed | March 31, 2026, 12:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b32eaf88190aae55aaeb963c50b |
completed | March 31, 2026, 5:27 a.m. |
| NEDg | Description generation | batch_69cb5f1c9ef08190b1b79482f39966c7 |
completed | March 31, 2026, 5:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cb767b198481909cfc1f7a44e6f0d8 |
completed | March 31, 2026, 7:23 a.m. |
Created at: March 30, 2026, 4:52 p.m.