Triple
T29539508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sykes |
E749450
|
entity |
| Predicate | hasNotableEpisodeType |
P138291
|
FINISHED |
| Object | remakes of earlier scripts |
—
|
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: remakes of earlier scripts | Statement: [Sykes, hasNotableEpisodeType, remakes of earlier scripts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableEpisodeType Context triple: [Sykes, hasNotableEpisodeType, remakes of earlier scripts]
-
A.
notableEpisodeTypes
chosen
Indicates that certain types or categories of episodes are especially significant or noteworthy in relation to the subject.
-
B.
hasNotableShow
Indicates that an entity is associated with a particular show that is considered notable or significant.
-
C.
hasNotableEpic
Indicates that an entity is associated with a particularly significant or distinguished epic work.
-
D.
hasEpisodeAbout
Indicates that a particular episode (such as of a show, podcast, or series) focuses on, discusses, or is centered around a specified subject or topic.
-
E.
hasEpisode
Indicates that something, typically a series or program, includes a specific episode as one of its constituent parts.
- 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_69f0bd47abb081909bd6e6a33d770fd8 |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69ffc704e1e88190884928a6a5c55a87 |
completed | May 9, 2026, 11:45 p.m. |
| PD | Predicate disambiguation | batch_69ffc6b483d881908ad872e25fa6abc5 |
completed | May 9, 2026, 11:43 p.m. |
Created at: April 28, 2026, 5:01 p.m.