Triple
T7771228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pete |
E179072
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
Bootleg Pete
Bootleg Pete is a classic Disney villain character, typically portrayed as a burly, antagonistic foil to Mickey Mouse and his friends.
|
E688242
|
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: Bootleg Pete | Statement: [Pete, alsoKnownAs, Bootleg Pete]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bootleg Pete Context triple: [Pete, alsoKnownAs, Bootleg Pete]
-
A.
Pistol Pete
Pistol Pete is the cowboy-themed mascot character representing New Mexico State University's athletic teams.
-
B.
Pistol Pete
Pistol Pete is the cowboy-themed costumed mascot of Oklahoma State University, inspired by Old West lawman Frank Eaton.
-
C.
Pistol Pete
Pistol Pete is the cowboy-themed mascot character representing the University of Wyoming’s athletic teams and school spirit.
-
D.
Gypsy Dave
Gypsy Dave was a close friend, collaborator, and traveling companion of Scottish singer-songwriter Donovan, often associated with the 1960s folk scene.
-
E.
Laffing Sal
Laffing Sal is a historic, animatronic laughing woman figure from early 20th-century amusement parks, now preserved as a popular attraction at San Francisco’s Musée Mécanique.
- 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: Bootleg Pete Triple: [Pete, alsoKnownAs, Bootleg Pete]
Generated description
Bootleg Pete is a classic Disney villain character, typically portrayed as a burly, antagonistic foil to Mickey Mouse and his friends.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bootleg Pete Target entity description: Bootleg Pete is a classic Disney villain character, typically portrayed as a burly, antagonistic foil to Mickey Mouse and his friends.
-
A.
Pistol Pete
Pistol Pete is the cowboy-themed mascot character representing New Mexico State University's athletic teams.
-
B.
Pistol Pete
Pistol Pete is the cowboy-themed mascot character representing the University of Wyoming’s athletic teams and school spirit.
-
C.
Pistol Pete
Pistol Pete is the cowboy-themed costumed mascot of Oklahoma State University, inspired by Old West lawman Frank Eaton.
-
D.
Gypsy Dave
Gypsy Dave was a close friend, collaborator, and traveling companion of Scottish singer-songwriter Donovan, often associated with the 1960s folk scene.
-
E.
Laffing Sal
Laffing Sal is a historic, animatronic laughing woman figure from early 20th-century amusement parks, now preserved as a popular attraction at San Francisco’s Musée Mécanique.
- 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c7045ebae88190a04c8f972795e615 |
completed | March 27, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8d6d65e308190924c05df5a0a4959 |
completed | March 29, 2026, 7:37 a.m. |
| NEDg | Description generation | batch_69c8d779769c8190a9be6fbc065156e0 |
completed | March 29, 2026, 7:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8d80a88f8819098bdb678e86f9be9 |
completed | March 29, 2026, 7:43 a.m. |
Created at: March 27, 2026, 4:11 p.m.