Triple
T2250740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wayne |
E49609
|
entity |
| Predicate | hasSpelling |
P457
|
FINISHED |
| Object |
W-a-y-n-e
W-a-y-n-e is a given name, typically a masculine first name or surname of English origin.
|
E246604
|
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: W-a-y-n-e | Statement: [Wayne, hasSpelling, W-a-y-n-e]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: W-a-y-n-e Context triple: [Wayne, hasSpelling, W-a-y-n-e]
-
A.
Mavis Leno
Mavis Leno is an American feminist and philanthropist known for her long-time activism, particularly in advocating for women's rights in Afghanistan.
-
B.
Wendy Williams
Wendy Williams is an American media personality and former radio DJ best known for hosting the syndicated television talk show "The Wendy Williams Show."
-
C.
Max Howell
Max Howell is a British software developer best known as the original creator of the popular macOS package manager Homebrew.
-
D.
Ellen DeGeneres
Ellen DeGeneres is an American comedian, actress, and television host best known for her groundbreaking sitcom "Ellen" and her long-running daytime talk show "The Ellen DeGeneres Show."
-
E.
Chrissy Teigen
Chrissy Teigen is an American model, television personality, and cookbook author known for her Sports Illustrated work, outspoken social media presence, and lifestyle brand.
- 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: W-a-y-n-e Triple: [Wayne, hasSpelling, W-a-y-n-e]
Generated description
W-a-y-n-e is a given name, typically a masculine first name or surname of English origin.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: W-a-y-n-e Target entity description: W-a-y-n-e is a given name, typically a masculine first name or surname of English origin.
-
A.
Mavis Leno
Mavis Leno is an American feminist and philanthropist known for her long-time activism, particularly in advocating for women's rights in Afghanistan.
-
B.
Wendy Williams
Wendy Williams is an American media personality and former radio DJ best known for hosting the syndicated television talk show "The Wendy Williams Show."
-
C.
Max Howell
Max Howell is a British software developer best known as the original creator of the popular macOS package manager Homebrew.
-
D.
Ellen DeGeneres
Ellen DeGeneres is an American comedian, actress, and television host best known for her groundbreaking sitcom "Ellen" and her long-running daytime talk show "The Ellen DeGeneres Show."
-
E.
Chrissy Teigen
Chrissy Teigen is an American model, television personality, and cookbook author known for her Sports Illustrated work, outspoken social media presence, and lifestyle brand.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc11b61888190af3b11b87dc8e0dc |
completed | March 7, 2026, 6:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6b1bc424819087b2ce9a6256a180 |
completed | March 9, 2026, 6:39 a.m. |
| NEDg | Description generation | batch_69ae6be0d108819085cf8c531d08db65 |
completed | March 9, 2026, 6:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae6c0fc220819090b254cc20b1bc26 |
completed | March 9, 2026, 6:43 a.m. |
Created at: March 4, 2026, 7:47 p.m.