Triple
T28321220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goodness Gracious Me |
E717283
|
entity |
| Predicate | numberOfTVEpisodes |
P2593
|
FINISHED |
| Object | 19 |
—
|
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: 19 | Statement: [Goodness Gracious Me, numberOfTVEpisodes, 19]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTVEpisodes Context triple: [Goodness Gracious Me, numberOfTVEpisodes, 19]
-
A.
numberOfEpisodes
chosen
Indicates the total count of episodes associated with a given entity, such as a series or season.
-
B.
televisionSeriesCount
Indicates the number of television series associated with or attributed to an entity.
-
C.
numberOfSeasons
Indicates the total count of seasons associated with a particular entity (such as a series, competition, or event).
-
D.
originallyPlannedNumberOfEpisodes
Indicates the total number of episodes that were initially intended or planned for a series or program before any changes occurred.
-
E.
hasEpisodeCountPerSeries
Indicates a relationship where a series is associated with the number of episodes it contains.
- 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_69eff6e6c3b08190ad78de6ba7f04548 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69ff5803c02c81908b63067119f5e684 |
completed | May 9, 2026, 3:51 p.m. |
| PD | Predicate disambiguation | batch_69ff576d8b308190b49a1e072a0ae661 |
completed | May 9, 2026, 3:49 p.m. |
Created at: April 28, 2026, 12:24 a.m.