Triple
T9060278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Love & Marriage: Huntsville |
E217102
|
entity |
| Predicate | episodeRuntimeApprox |
P11339
|
FINISHED |
| Object | 60 minutes including commercials |
—
|
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: 60 minutes including commercials | Statement: [Love & Marriage: Huntsville, episodeRuntimeApprox, 60 minutes including commercials]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: episodeRuntimeApprox Context triple: [Love & Marriage: Huntsville, episodeRuntimeApprox, 60 minutes including commercials]
-
A.
hasEpisodeRuntime
chosen
Indicates the duration of time that each individual episode of a series or show runs.
-
B.
filmRuntimeApprox
Indicates an approximate or estimated duration of a film, rather than its exact runtime.
-
C.
runningTimeMiniSeriesVersion
Indicates the duration of the mini-series version of a work, typically measured in time units such as minutes.
-
D.
filmRuntimeMinutes
Indicates the duration of a film expressed in minutes.
-
E.
numberOfEpisodes
Indicates the total count of episodes associated with a given entity, such as a 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_69ca83d4425481909a319dab847724ec |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc7eca6d8c8190b1a11a60d6649f78 |
completed | April 1, 2026, 2:11 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee6d83c819095d8ed0779aa8511 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:10 p.m.