Triple
T13780003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fashion Police |
E331107
|
entity |
| Predicate | recurringSegment |
P58011
|
FINISHED |
| Object | Best Dressed |
—
|
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: Best Dressed | Statement: [Fashion Police, recurringSegment, Best Dressed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recurringSegment Context triple: [Fashion Police, recurringSegment, Best Dressed]
-
A.
recurringSegmentOn
chosen
Indicates that one entity appears repeatedly as a regular segment or feature within another entity, such as a show, publication, or series.
-
B.
recurringDuring
Indicates that an event or state happens repeatedly within the time span or context defined by another event or interval.
-
C.
recurringEvent
Indicates that an event occurs repeatedly over time according to some regular pattern or schedule.
-
D.
recurringStatus
Indicates that an event, action, or state occurs repeatedly over time according to a defined recurrence pattern or schedule.
-
E.
recurringLocation
Indicates that an event, action, or state happens repeatedly at the specified location over time.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02460a688190a27874f8d35819c7 |
completed | April 14, 2026, 9 a.m. |
| PD | Predicate disambiguation | batch_69dbbe97846c819093b00ea117b64e0d |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 10:11 p.m.