Triple
T35709328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pantomimes lumineuses |
E1031808
|
entity |
| Predicate | screeningFrequency |
P55256
|
FINISHED |
| Object | regularly shown at Musée Grévin |
—
|
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: regularly shown at Musée Grévin | Statement: [Pantomimes lumineuses, screeningFrequency, regularly shown at Musée Grévin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screeningFrequency Context triple: [Pantomimes lumineuses, screeningFrequency, regularly shown at Musée Grévin]
-
A.
inspectionFrequency
Indicates how often an entity is examined, checked, or reviewed within a given time period.
-
B.
appointmentFrequency
Indicates how often appointments are scheduled or expected to occur within a given time period.
-
C.
serviceFrequencyType
chosen
Indicates how often a service occurs or is scheduled within a given time period.
-
D.
screeningType
Indicates the specific method or category of screening applied in a screening process or evaluation.
-
E.
recommendedUsageFrequency
Indicates how often something is advised or prescribed to be used within a given time period.
- 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_69f76e0df1d08190965b1c6dff94c391 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d219f8819081dc4ce3c83ca0cb |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:05 p.m.