Triple
T3573843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UCLA Bruins women's gymnastics |
E75638
|
entity |
| Predicate | hasNotableAlumna |
P51
|
FINISHED |
| Object |
Felicia Hano
Felicia Hano is an American artistic gymnast and former elite competitor who became a standout collegiate gymnast for the UCLA Bruins.
|
E368844
|
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: Felicia Hano | Statement: [UCLA Bruins women's gymnastics, hasNotableAlumna, Felicia Hano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Felicia Hano Context triple: [UCLA Bruins women's gymnastics, hasNotableAlumna, Felicia Hano]
-
A.
Felicia Lyn
Felicia Lyn is known as the wife of former Panamanian military ruler Manuel Noriega.
-
B.
Helen Lasichanh
Helen Lasichanh is a Laotian-Ethiopian model, designer, and stylist known for her distinctive fashion sense and marriage to musician Pharrell Williams.
-
C.
Juanita Vanoy
Juanita Vanoy is a former model and Chicago-based real estate professional best known as the ex-wife of basketball legend Michael Jordan.
-
D.
Lusiana Burchard
Lusiana Burchard is best known as the wife of renowned mathematician Sir Michael Atiyah.
-
E.
Louise Fazenda
Louise Fazenda was a prominent American silent film comedian and character actress known for her work in early Hollywood comedies.
- 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: Felicia Hano Triple: [UCLA Bruins women's gymnastics, hasNotableAlumna, Felicia Hano]
Generated description
Felicia Hano is an American artistic gymnast and former elite competitor who became a standout collegiate gymnast for the UCLA Bruins.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Felicia Hano Target entity description: Felicia Hano is an American artistic gymnast and former elite competitor who became a standout collegiate gymnast for the UCLA Bruins.
-
A.
Felicia Lyn
Felicia Lyn is known as the wife of former Panamanian military ruler Manuel Noriega.
-
B.
Helen Lasichanh
Helen Lasichanh is a Laotian-Ethiopian model, designer, and stylist known for her distinctive fashion sense and marriage to musician Pharrell Williams.
-
C.
Juanita Vanoy
Juanita Vanoy is a former model and Chicago-based real estate professional best known as the ex-wife of basketball legend Michael Jordan.
-
D.
Lusiana Burchard
Lusiana Burchard is best known as the wife of renowned mathematician Sir Michael Atiyah.
-
E.
Louise Fazenda
Louise Fazenda was a prominent American silent film comedian and character actress known for her work in early Hollywood comedies.
- 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_69ad85d5e3008190bdfe0bacdd1f5a1b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0d928f08190830347b3b032178a |
completed | March 8, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bbc077048190917260402e2ccf66 |
completed | March 13, 2026, 7:24 a.m. |
| NEDg | Description generation | batch_69b3bc556d2881908867daf93b509d74 |
completed | March 13, 2026, 7:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3f5b6e66c81908700d5f3df0a864d |
completed | March 13, 2026, 11:32 a.m. |
Created at: March 8, 2026, 3:21 p.m.