Triple
T239166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Ontario Museum |
E4889
|
entity |
| Predicate | hasArchitect |
P184
|
FINISHED |
| Object |
Darling and Pearson
Darling and Pearson was a prominent early 20th-century Canadian architectural firm known for designing major institutional and public buildings, particularly in Toronto.
|
E30772
|
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: Darling and Pearson | Statement: [Royal Ontario Museum, hasArchitect, Darling and Pearson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Darling and Pearson Context triple: [Royal Ontario Museum, hasArchitect, Darling and Pearson]
-
A.
Linda
Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 20th century.
-
B.
Clemmie
Clemmie is a diminutive given name commonly used as a nickname for Clementine.
-
C.
Nance
Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
-
D.
The Sisters
"The Sisters" is a Caroline-era stage comedy by English playwright James Shirley, known for its witty exploration of family, marriage, and social manners.
-
E.
Elliott
Elliott is a masculine given name of English origin, often used as both a first name and a surname.
- 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: Darling and Pearson Triple: [Royal Ontario Museum, hasArchitect, Darling and Pearson]
Generated description
Darling and Pearson was a prominent early 20th-century Canadian architectural firm known for designing major institutional and public buildings, particularly in Toronto.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Darling and Pearson Target entity description: Darling and Pearson was a prominent early 20th-century Canadian architectural firm known for designing major institutional and public buildings, particularly in Toronto.
-
A.
Linda
Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 20th century.
-
B.
Clemmie
Clemmie is a diminutive given name commonly used as a nickname for Clementine.
-
C.
Nance
Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
-
D.
The Sisters
"The Sisters" is a Caroline-era stage comedy by English playwright James Shirley, known for its witty exploration of family, marriage, and social manners.
-
E.
Elliott
Elliott is a masculine given name of English origin, often used as both a first name and a surname.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25ceaecdc81909e9ff49cb6a4e02a |
completed | Feb. 28, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a36735dff881908ed9cefbf6f5e09d |
completed | Feb. 28, 2026, 10:07 p.m. |
| NEDg | Description generation | batch_69a367d82e9c8190ae1bcc99232a24fa |
completed | Feb. 28, 2026, 10:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3689a2970819087306bff495a4a3a |
completed | Feb. 28, 2026, 10:13 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.