Triple
T22560127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark R. Hughes |
E557788
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Suzy Bear
Suzy Bear is known as the spouse of Mark R. Hughes, the founder of the global nutrition company Herbalife.
|
E1543445
|
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: Suzy Bear | Statement: [Mark R. Hughes, spouse, Suzy Bear]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suzy Bear Context triple: [Mark R. Hughes, spouse, Suzy Bear]
-
A.
Mellie the Bear
Mellie the Bear is the mascot of Pauli Murray College, one of Yale University's residential colleges.
-
B.
Brisky the Bear
Brisky the Bear is the costumed bear mascot of Japan’s Hokkaido Nippon-Ham Fighters professional baseball team, known for entertaining fans at games and team events.
-
C.
Cindy Bear
Cindy Bear is a sweet, soft-spoken female bear from the Yogi Bear cartoon series, often portrayed as Yogi’s love interest in Jellystone Park.
-
D.
Boomer the Bear
Boomer the Bear is the costumed bear mascot who represents Missouri State University at athletic events and campus activities.
-
E.
Sylvester Apollo Bear
Sylvester Apollo Bear is the son of American model and actress Emily Ratajkowski and film producer Sebastian Bear-McClard.
- 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: Suzy Bear Triple: [Mark R. Hughes, spouse, Suzy Bear]
Generated description
Suzy Bear is known as the spouse of Mark R. Hughes, the founder of the global nutrition company Herbalife.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Suzy Bear Target entity description: Suzy Bear is known as the spouse of Mark R. Hughes, the founder of the global nutrition company Herbalife.
-
A.
Mellie the Bear
Mellie the Bear is the mascot of Pauli Murray College, one of Yale University's residential colleges.
-
B.
Brisky the Bear
Brisky the Bear is the costumed bear mascot of Japan’s Hokkaido Nippon-Ham Fighters professional baseball team, known for entertaining fans at games and team events.
-
C.
Cindy Bear
Cindy Bear is a sweet, soft-spoken female bear from the Yogi Bear cartoon series, often portrayed as Yogi’s love interest in Jellystone Park.
-
D.
Boomer the Bear
Boomer the Bear is the costumed bear mascot who represents Missouri State University at athletic events and campus activities.
-
E.
Sylvester Apollo Bear
Sylvester Apollo Bear is the son of American model and actress Emily Ratajkowski and film producer Sebastian Bear-McClard.
- 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_69e11e59db848190b4272ecd2b690ffd |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f7c914881909584c46ae323c779 |
completed | April 29, 2026, 1:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b2d6a72448190bddf2e280b6fca76 |
completed | May 18, 2026, 3:16 p.m. |
| NEDg | Description generation | batch_6a0b364718308190937c3b7ae90ea9df |
completed | May 18, 2026, 3:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b37a1ecc08190ac89e862582833a0 |
completed | May 18, 2026, 4 p.m. |
Created at: April 16, 2026, 8:52 p.m.