Triple
T20248193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Master of None |
E498478
|
entity |
| Predicate | hasEpisodeCountPerSeason |
P2593
|
FINISHED |
| Object | Season 1: 10 episodes |
—
|
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: Season 1: 10 episodes | Statement: [Master of None, hasEpisodeCountPerSeason, Season 1: 10 episodes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEpisodeCountPerSeason Context triple: [Master of None, hasEpisodeCountPerSeason, Season 1: 10 episodes]
-
A.
hasEpisodeCountPerSeries
Indicates a relationship where a series is associated with the number of episodes it contains.
-
B.
hasEpisodeCountInFirstSeries
Indicates that an entity has a specific number of episodes in its first series or season.
-
C.
numberOfSeriesPerSeason
Indicates the total count of series (or episodes/instalments) that occur within a single season of something.
-
D.
numberOfEpisodes
chosen
Indicates the total count of episodes associated with a given entity, such as a series or season.
-
E.
numberOfSeasons
Indicates the total count of seasons associated with a particular entity (such as a series, competition, or event).
- 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e673a5ce4081908dff86ed4c613fd6 |
completed | April 20, 2026, 6:42 p.m. |
| PD | Predicate disambiguation | batch_69e55b1b23f88190bdcbe2f81dd226dd |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:40 p.m.