Triple
T30343874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Dress-Up Darling |
E771822
|
entity |
| Predicate | episodesCount |
P2593
|
FINISHED |
| Object | 12 |
—
|
LITERAL 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: 12 | Statement: [My Dress-Up Darling, episodesCount, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: episodesCount Context triple: [My Dress-Up Darling, episodesCount, 12]
-
A.
numberOfEpisodes
chosen
Indicates the total count of episodes associated with a given entity, such as a series or season.
-
B.
partOfEpisodeCount
Indicates that one entity specifies the number of episodes contained within another entity (such as a season, series, or collection).
-
C.
hasEpisodeCountPerSeries
Indicates a relationship where a series is associated with the number of episodes it contains.
-
D.
hasNumberOfEPs
Indicates the quantity or count of EPs (e.g., episodes, extended plays, or similar units) associated with an entity.
-
E.
notableEpisodeCount
Indicates the number of episodes in which the subject is notably featured or recognized.
- F. None of above.
Provenance (3 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_69f2248b9a208190bc3e6804acd5afd6 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f682065a548190bff089d7dafbf3ad |
completed | May 2, 2026, 11 p.m. |
| PD | Predicate disambiguation | batch_69f67603526c81908295a1ece8727c66 |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 29, 2026, 7:55 p.m.