Triple
T26503409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted Shackelford |
E669477
|
entity |
| Predicate | hasNotableTelevisionAppearance |
P98533
|
FINISHED |
| Object | Knots Landing |
—
|
NE NERFINISHED |
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: Knots Landing | Statement: [Ted Shackelford, hasNotableTelevisionAppearance, Knots Landing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableTelevisionAppearance Context triple: [Ted Shackelford, hasNotableTelevisionAppearance, Knots Landing]
-
A.
hasNotableTelevisionSeries
Indicates that an entity is associated with one or more television series that are considered notable or significant.
-
B.
hasNotableMultipleAppearances
Indicates that an entity appears multiple times in a context or medium in a way considered significant or noteworthy.
-
C.
hasNotableShow
chosen
Indicates that an entity is associated with a particular show that is considered notable or significant.
-
D.
notableAppearanceIn
Indicates that an entity is prominently featured or plays a significant role in a particular work, event, or context.
-
E.
hasNotableEarlyCareerAppearanceOf
Indicates that an entity features or includes a significant appearance by another entity during the latter’s early career.
- 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_69eeb319ec70819090834c2591cf5f1e |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69fd7e364a648190a1e9e1d9fc76e99e |
completed | May 8, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69fd7bb547608190a3b04dddbca6b8bc |
completed | May 8, 2026, 5:59 a.m. |
Created at: April 27, 2026, 1:14 a.m.