Triple
T24196974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Korean television dramas |
E599860
|
entity |
| Predicate | typicalEpisodeCount |
P110039
|
FINISHED |
| Object | 16 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: 16 episodes | Statement: [South Korean television dramas, typicalEpisodeCount, 16 episodes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalEpisodeCount Context triple: [South Korean television dramas, typicalEpisodeCount, 16 episodes]
-
A.
numberOfEpisodes
Indicates the total count of episodes associated with a given entity, such as a series or season.
-
B.
segmentCountPerEpisode
Indicates the number of distinct segments contained within a single episode.
-
C.
notableEpisodeCount
Indicates the number of episodes in which the subject is notably featured or recognized.
-
D.
hasEpisodeCountPerSeries
chosen
Indicates a relationship where a series is associated with the number of episodes it contains.
-
E.
hasEpisodeCountInFirstSeries
Indicates that an entity has a specific number of episodes in its first series or season.
- 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_69e288ceaab88190899d0acb5931591d |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1e24c103481908ea49dd8e77dee32 |
completed | April 29, 2026, 10:49 a.m. |
| PD | Predicate disambiguation | batch_69f1c43e55688190b55fc20274ed471c |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:36 p.m.