Triple
T12503749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vatican Sayings |
E298892
|
entity |
| Predicate | hasNumberOfSayings |
P50925
|
FINISHED |
| Object | approximately 81 |
—
|
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: approximately 81 | Statement: [Vatican Sayings, hasNumberOfSayings, approximately 81]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfSayings Context triple: [Vatican Sayings, hasNumberOfSayings, approximately 81]
-
A.
numberOfAphorisms
chosen
Indicates the quantity or count of aphorisms associated with a given entity.
-
B.
hasSayingTheme
Indicates that a saying, proverb, or quoted expression is about or centers on a particular theme or subject.
-
C.
includesSaying
Indicates that one entity (such as a text, speech, or communication) contains or incorporates a particular saying, phrase, or quoted expression.
-
D.
keySaying
Indicates that an entity is a notable or characteristic saying, phrase, or quotation associated with another entity.
-
E.
hasNumberOfKoans
Indicates the relationship that specifies how many koans are associated with or contained by a given entity.
- 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_69d6ada4cd388190ae3bbf83ff87057a |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94dfcea188190a929db1aabe1a286 |
completed | April 10, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_69d94d43b7008190af2648fe09fd6d23 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:57 p.m.