Triple
T32993180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | beguine court |
E844146
|
entity |
| Predicate | hasVowRequirement |
P180458
|
FINISHED |
| Object | no permanent vows required |
—
|
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: no permanent vows required | Statement: [beguine court, hasVowRequirement, no permanent vows required]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVowRequirement Context triple: [beguine court, hasVowRequirement, no permanent vows required]
-
A.
hasVow
Indicates that one entity has made or is bound by a formal vow or promise in relation to another entity or context.
-
B.
hasVowelLetter
Indicates that something contains at least one vowel character within its letters.
-
C.
containsVowelLetters
Indicates that the subject includes one or more vowel letters within its sequence of characters.
-
D.
hasNumberOfVowelLetters
Indicates that an entity is associated with a specific count of vowel letters it contains.
-
E.
hasVowelFeature
Indicates that an entity possesses a specific vowel-related phonological or articulatory feature.
- F. None of above. chosen
Provenance (4 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_69f3494d99988190b502c68926af2c4d |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f74062b9388190b30546cf700a825c |
completed | May 3, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69f73c802b848190b61a416b7488bd96 |
completed | May 3, 2026, 12:16 p.m. |
| PDg | Predicate description generation | batch_69f74061c440819080434155c2d60341 |
completed | May 3, 2026, 12:32 p.m. |
Created at: May 1, 2026, 1:22 a.m.