Triple
T14959648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyle Waggoner |
E373030
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Waggoner
Waggoner is a surname most notably associated with American actor and model Lyle Waggoner, known for his work on The Carol Burnett Show and the TV series Wonder Woman.
|
E1128971
|
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: Waggoner | Statement: [Lyle Waggoner, familyName, Waggoner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Waggoner Context triple: [Lyle Waggoner, familyName, Waggoner]
-
A.
Wagoner
Wagoner is a small city in northeastern Oklahoma that serves as the administrative and commercial center of Wagoner County.
-
B.
Waggaman
Waggaman is a suburban community in southeastern Louisiana, located along the Mississippi River within the New Orleans metropolitan area.
-
C.
Scoggins
Scoggins is a minor supporting character in the 1999 science fiction horror film "Deep Blue Sea," which centers on genetically enhanced sharks in an underwater research facility.
-
D.
Waddy
Waddy is a given name most notably borne by the American architect Waddy Butler Wood.
-
E.
O'Steen
O'Steen is a surname most notably associated with American film editor Sam O'Steen, known for his work on several acclaimed Hollywood films.
- 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: Waggoner Triple: [Lyle Waggoner, familyName, Waggoner]
Generated description
Waggoner is a surname most notably associated with American actor and model Lyle Waggoner, known for his work on The Carol Burnett Show and the TV series Wonder Woman.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Waggoner Target entity description: Waggoner is a surname most notably associated with American actor and model Lyle Waggoner, known for his work on The Carol Burnett Show and the TV series Wonder Woman.
-
A.
Wagoner
Wagoner is a small city in northeastern Oklahoma that serves as the administrative and commercial center of Wagoner County.
-
B.
Waggaman
Waggaman is a suburban community in southeastern Louisiana, located along the Mississippi River within the New Orleans metropolitan area.
-
C.
Scoggins
Scoggins is a minor supporting character in the 1999 science fiction horror film "Deep Blue Sea," which centers on genetically enhanced sharks in an underwater research facility.
-
D.
Waddy
Waddy is a given name most notably borne by the American architect Waddy Butler Wood.
-
E.
O'Steen
O'Steen is a surname most notably associated with American film editor Sam O'Steen, known for his work on several acclaimed Hollywood films.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cd85bc81909040b7ff78f62554 |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7ea1d78c81909b877fda05ef9231 |
completed | May 9, 2026, 12:24 a.m. |
| NEDg | Description generation | batch_69fe7ff5c84c8190b3e97f09633bf181 |
completed | May 9, 2026, 12:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe80be100481908dcf07b683fc1411 |
completed | May 9, 2026, 12:33 a.m. |
Created at: April 10, 2026, 2:40 a.m.