Triple
T20022785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gresham College |
E494903
|
entity |
| Predicate | hasNotableProfessor |
P13831
|
FINISHED |
| Object |
Linda Yueh
Linda Yueh is a British economist, broadcaster, and author known for her work on global economic policy and for presenting and commenting on economic issues in the media.
|
E1406770
|
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: Linda Yueh | Statement: [Gresham College, hasNotableProfessor, Linda Yueh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Linda Yueh Context triple: [Gresham College, hasNotableProfessor, Linda Yueh]
-
A.
Rita Hsiao
Rita Hsiao is a screenwriter best known for her work on animated feature films, including co-writing Pixar's "Toy Story 2."
-
B.
ViviAnn Yee
ViviAnn Yee is an American child voice actress known for her roles in animated films and television series, including work in the Boss Baby franchise.
-
C.
Jennifer Lien
Jennifer Lien is an American actress best known for her role as Kes on the television series "Star Trek: Voyager."
-
D.
Linda Cho
Linda Cho is a Tony Award–winning costume designer known for her work on major Broadway productions and other theatrical performances.
-
E.
Yvonne Chu
Yvonne Chu is the wife of Nobel Prize–winning physicist and former U.S. Secretary of Energy Steven Chu.
- 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: Linda Yueh Triple: [Gresham College, hasNotableProfessor, Linda Yueh]
Generated description
Linda Yueh is a British economist, broadcaster, and author known for her work on global economic policy and for presenting and commenting on economic issues in the media.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Linda Yueh Target entity description: Linda Yueh is a British economist, broadcaster, and author known for her work on global economic policy and for presenting and commenting on economic issues in the media.
-
A.
Rita Hsiao
Rita Hsiao is a screenwriter best known for her work on animated feature films, including co-writing Pixar's "Toy Story 2."
-
B.
ViviAnn Yee
ViviAnn Yee is an American child voice actress known for her roles in animated films and television series, including work in the Boss Baby franchise.
-
C.
Jennifer Lien
Jennifer Lien is an American actress best known for her role as Kes on the television series "Star Trek: Voyager."
-
D.
Linda Cho
Linda Cho is a Tony Award–winning costume designer known for her work on major Broadway productions and other theatrical performances.
-
E.
Yvonne Chu
Yvonne Chu is the wife of Nobel Prize–winning physicist and former U.S. Secretary of Energy Steven Chu.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66288fc18819083833b55c5e069a6 |
completed | April 20, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a080e2d32008190a770addba6e44adb |
completed | May 16, 2026, 6:26 a.m. |
| NEDg | Description generation | batch_6a080ec9c56481908b69834b5a1ae105 |
completed | May 16, 2026, 6:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a080f6e218c8190b4c7b0d5de9f984c |
completed | May 16, 2026, 6:32 a.m. |
Created at: April 11, 2026, 3:35 p.m.