Triple
T19359101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Name of the Game |
E484227
|
entity |
| Predicate | episodeLengthCategory |
P34960
|
FINISHED |
| Object | feature-length television 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: feature-length television episodes | Statement: [The Name of the Game, episodeLengthCategory, feature-length television episodes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: episodeLengthCategory Context triple: [The Name of the Game, episodeLengthCategory, feature-length television episodes]
-
A.
hasEpisodeLengthType
chosen
Indicates the type or category of duration associated with an episode (e.g., standard length, short, extended).
-
B.
televisionSeriesRuntimeCharacteristic
Indicates a relationship where a television series is associated with a specific runtime-related characteristic, such as typical episode length or overall duration pattern.
-
C.
hasEpisodeRuntime
Indicates the duration of time that each individual episode of a series or show runs.
-
D.
seriesLengthMinimum
Indicates that the length of a series must be at least a specified minimum value.
-
E.
filmLength
Indicates the duration or running time of a film, typically measured in units such as minutes.
- 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_69d8e8d305088190ad13571532aa454c |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e6190b343c81909734ba776fd196dc |
completed | April 20, 2026, 12:16 p.m. |
| PD | Predicate disambiguation | batch_69e4dd13a8cc81909cd02668564c9f29 |
completed | April 19, 2026, 1:48 p.m. |
Created at: April 10, 2026, 1:34 p.m.