Triple
T12891653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wayne Robson |
E308380
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Red Green
Red Green is a fictional, duct-tape-loving handyman and host of the Canadian comedy series "The Red Green Show," known for his deadpan humor and absurd DIY inventions.
|
E1009717
|
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: Red Green | Statement: [Wayne Robson, notableWork, Red Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Red Green Context triple: [Wayne Robson, notableWork, Red Green]
-
A.
Red V
Red V is the iconic nickname and jersey motif associated with the St George Illawarra Dragons rugby league club.
-
B.
Orange
Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
-
C.
Orange
Orange was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
-
D.
Orange
Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
-
E.
Orange
Orange is the nickname and primary identity of Syracuse University's athletic teams, especially its prominent men's basketball program.
- 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: Red Green Triple: [Wayne Robson, notableWork, Red Green]
Generated description
Red Green is a fictional, duct-tape-loving handyman and host of the Canadian comedy series "The Red Green Show," known for his deadpan humor and absurd DIY inventions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Red Green Target entity description: Red Green is a fictional, duct-tape-loving handyman and host of the Canadian comedy series "The Red Green Show," known for his deadpan humor and absurd DIY inventions.
-
A.
Red V
Red V is the iconic nickname and jersey motif associated with the St George Illawarra Dragons rugby league club.
-
B.
Orange
Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
-
C.
Orange
Orange was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
-
D.
Orange
Orange is the nickname and primary identity of Syracuse University's athletic teams, especially its prominent men's basketball program.
-
E.
Orange
Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97146d2208190be5ae26e51193b67 |
completed | April 10, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a55be3288190b2bc0bd197431db3 |
completed | May 3, 2026, 1:31 a.m. |
| NEDg | Description generation | batch_69f6a616f6e4819096c9850434882548 |
completed | May 3, 2026, 1:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6a716bb2c81909dccc5ddbf3c92b5 |
completed | May 3, 2026, 1:38 a.m. |
Created at: April 9, 2026, 5:39 p.m.