Triple
T20909780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | union conferences of the Seventh-day Adventist Church |
E514906
|
entity |
| Predicate | meetsHowOften |
P2557
|
FINISHED |
| Object | constituency sessions typically every 3 to 5 years |
—
|
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: constituency sessions typically every 3 to 5 years | Statement: [union conferences of the Seventh-day Adventist Church, meetsHowOften, constituency sessions typically every 3 to 5 years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meetsHowOften Context triple: [union conferences of the Seventh-day Adventist Church, meetsHowOften, constituency sessions typically every 3 to 5 years]
-
A.
meetingFrequency
chosen
Indicates how often a meeting or recurring gathering takes place over a given period.
-
B.
meetsRegularly
Indicates that two or more entities come together on a recurring or scheduled basis.
-
C.
appointmentFrequency
Indicates how often appointments are scheduled or expected to occur within a given time period.
-
D.
meetsEvery
Indicates that one entity encounters or comes into contact with every member of a specified set of entities.
-
E.
serviceFrequencyType
Indicates how often a service occurs or is scheduled 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_69e0b4f8a1108190bce3d31331290ced |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6ec5d73c88190a48180a1eed88190 |
completed | April 21, 2026, 3:17 a.m. |
| PD | Predicate disambiguation | batch_69e5c9ac91108190a6700fcdf2f11890 |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:48 p.m.