Triple
T23758720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Duchess of Duke Street |
E587189
|
entity |
| Predicate | hasRuntimePerEpisodeApprox |
P11339
|
FINISHED |
| Object | 50 minutes |
—
|
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: 50 minutes | Statement: [The Duchess of Duke Street, hasRuntimePerEpisodeApprox, 50 minutes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRuntimePerEpisodeApprox Context triple: [The Duchess of Duke Street, hasRuntimePerEpisodeApprox, 50 minutes]
-
A.
hasEpisodeRuntime
chosen
Indicates the duration of time that each individual episode of a series or show runs.
-
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.
hasEpisodeLengthType
Indicates the type or category of duration associated with an episode (e.g., standard length, short, extended).
-
D.
numberOfEpisodes
Indicates the total count of episodes associated with a given entity, such as a series or season.
-
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_69e2490a0eec81908cdef8a862828d7a |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1bdaf52848190b034208de4c4e01a |
completed | April 29, 2026, 8:13 a.m. |
| PD | Predicate disambiguation | batch_69f155f79e34819080f9ddb972b34deb |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:14 p.m.