Triple
T14534308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Culp as Kelly Robinson |
E340997
|
entity |
| Predicate | frequentSetting |
P89047
|
FINISHED |
| Object | various international locations |
—
|
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: various international locations | Statement: [Robert Culp as Kelly Robinson, frequentSetting, various international locations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequentSetting Context triple: [Robert Culp as Kelly Robinson, frequentSetting, various international locations]
-
A.
frequentSettingType
Indicates that an entity commonly or regularly occurs, operates, or is used in a particular type of setting or environment.
-
B.
isFrequently
Indicates that an action, state, or relationship occurs often or with high regularity between the related entities.
-
C.
frequentOccasion
Indicates that a particular event, situation, or condition occurs repeatedly or commonly over time.
-
D.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
-
E.
coversSetting
chosen
Indicates that one entity includes or addresses a particular setting or context within its scope.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea053f9bc8190901b9d321811d881 |
completed | April 14, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69de5c546c7081909e27d504ec360c5c |
completed | April 14, 2026, 3:25 p.m. |
Created at: April 10, 2026, 1:22 a.m.