Triple
T9739612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donnell Rawlings |
E236151
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Guy Code
Guy Code is an MTV2 comedy series in which comedians and entertainers humorously explain and debate the unspoken rules and behaviors expected of men.
|
E818727
|
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: Guy Code | Statement: [Donnell Rawlings, notableWork, Guy Code]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guy Code Context triple: [Donnell Rawlings, notableWork, Guy Code]
-
A.
Cheers
Cheers is a beloved American sitcom set in a Boston bar, renowned for its witty ensemble cast, character-driven humor, and significant influence on television comedy.
-
B.
Guys with Kids
Guys with Kids is an American sitcom that follows three thirty-something fathers navigating the challenges and humor of modern parenthood.
-
C.
Guy
Guy is an influential American R&B group, central to the development of the new jack swing sound in the late 1980s and early 1990s.
-
D.
Guy
Guy is a masculine given name of French origin that has been widely used in English-speaking countries.
-
E.
Gro
Gro is the given name of Gro Harlem Brundtland, the Norwegian physician and politician who served three terms as Prime Minister of Norway and later led the World Health Organization.
- 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: Guy Code Triple: [Donnell Rawlings, notableWork, Guy Code]
Generated description
Guy Code is an MTV2 comedy series in which comedians and entertainers humorously explain and debate the unspoken rules and behaviors expected of men.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Guy Code Target entity description: Guy Code is an MTV2 comedy series in which comedians and entertainers humorously explain and debate the unspoken rules and behaviors expected of men.
-
A.
Cheers
Cheers is a beloved American sitcom set in a Boston bar, renowned for its witty ensemble cast, character-driven humor, and significant influence on television comedy.
-
B.
Guys with Kids
Guys with Kids is an American sitcom that follows three thirty-something fathers navigating the challenges and humor of modern parenthood.
-
C.
Guy
Guy is an influential American R&B group, central to the development of the new jack swing sound in the late 1980s and early 1990s.
-
D.
Guy
Guy is a masculine given name of French origin that has been widely used in English-speaking countries.
-
E.
Gro
Gro is the given name of Gro Harlem Brundtland, the Norwegian physician and politician who served three terms as Prime Minister of Norway and later led the World Health Organization.
- 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_69ca84d313e88190983ee6ffd0ef60d2 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9ef43fec8190987628f401a27436 |
completed | April 1, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1afe0dab48190832ab77265c09d70 |
completed | April 5, 2026, 12:42 a.m. |
| NEDg | Description generation | batch_69d1b08ba1f48190830852f9d60e3368 |
completed | April 5, 2026, 12:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1b124659481909e7a2ecaf01d8a50 |
completed | April 5, 2026, 12:47 a.m. |
Created at: March 30, 2026, 8:22 p.m.