Triple
T20022763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gresham College |
E494903
|
entity |
| Predicate | hasNotableProfessor |
P13831
|
FINISHED |
| Object |
Sarah Hart
Sarah Hart is a British mathematician known for her work in algebra and for popularizing mathematics through public lectures and writing.
|
E1406758
|
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: Sarah Hart | Statement: [Gresham College, hasNotableProfessor, Sarah Hart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Hart Context triple: [Gresham College, hasNotableProfessor, Sarah Hart]
-
A.
Mary Ellis
Mary Ellis was a British actress known for her work on stage and screen in the early to mid-20th century.
-
B.
Harriet Davies
Harriet Davies is an actress known for her role in the British thriller web series "Girl Number 9."
-
C.
Harriet Pitt
Harriet Pitt was an 18th-century British actress and the daughter of statesman William Pitt the Elder.
-
D.
Harriet Pitt
Harriet Pitt was an 18th-century British actress known for her work on the London stage and as the mother of actor and playwright Charles Dibdin the younger.
-
E.
Harriet Burns
Harriet Burns was a pioneering Disney artist and model maker, renowned as the first woman hired in a creative role at Walt Disney Imagineering and for her work on Disneyland attractions.
- 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: Sarah Hart Triple: [Gresham College, hasNotableProfessor, Sarah Hart]
Generated description
Sarah Hart is a British mathematician known for her work in algebra and for popularizing mathematics through public lectures and writing.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sarah Hart Target entity description: Sarah Hart is a British mathematician known for her work in algebra and for popularizing mathematics through public lectures and writing.
-
A.
Mary Ellis
Mary Ellis was a British actress known for her work on stage and screen in the early to mid-20th century.
-
B.
Harriet Davies
Harriet Davies is an actress known for her role in the British thriller web series "Girl Number 9."
-
C.
Harriet Pitt
Harriet Pitt was an 18th-century British actress and the daughter of statesman William Pitt the Elder.
-
D.
Harriet Pitt
Harriet Pitt was an 18th-century British actress known for her work on the London stage and as the mother of actor and playwright Charles Dibdin the younger.
-
E.
Harriet Burns
Harriet Burns was a pioneering Disney artist and model maker, renowned as the first woman hired in a creative role at Walt Disney Imagineering and for her work on Disneyland attractions.
- 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.