Triple
T35067993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady & Peebles |
E1011783
|
entity |
| Predicate | featuresRoyalCharacter |
P35285
|
FINISHED |
| Object | Princess Bubblegum |
E309303
|
NE FINISHED |
How this triple was built (2 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: Princess Bubblegum | Statement: [Lady & Peebles, featuresRoyalCharacter, Princess Bubblegum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresRoyalCharacter Context triple: [Lady & Peebles, featuresRoyalCharacter, Princess Bubblegum]
-
A.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
-
B.
featuresCharactersFrom
Indicates that one entity (such as a work or production) includes or presents characters originating from another entity.
-
C.
featuresPrincess
chosen
Indicates that something includes or prominently presents a princess as a central element or character.
-
D.
featuresReturningCharacterFrom
Indicates that a work includes the reappearance of a character who previously appeared in the referenced source work.
-
E.
featuresCharacterRescue
Indicates that a work includes a scene or storyline in which one character rescues or saves another.
- F. None of above.
Provenance (4 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_69f76dd193108190af2528186f25b72a |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3d1777998081909f67cfb4bf219b76 |
completed | June 25, 2026, 11:56 a.m. |
| PD | Predicate disambiguation | batch_6a037a016960819093ed4990fb4d9d36 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:01 p.m.