Triple
T5649499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Church of Pater Noster |
E124466
|
entity |
| Predicate | languageDisplayCount |
P36891
|
FINISHED |
| Object | many languages |
—
|
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: many languages | Statement: [Church of Pater Noster, languageDisplayCount, many languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageDisplayCount Context triple: [Church of Pater Noster, languageDisplayCount, many languages]
-
A.
displayCount
Indicates the number of times something is shown or presented, typically within a given context or interface.
-
B.
languagePanelCount
Indicates the number of language panels associated with or present in a given context or entity.
-
C.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
D.
currentNumberOfLanguages
chosen
Indicates the present count of distinct languages associated with or used by a given entity.
-
E.
elementCountDescription
Indicates a description of how many elements are present, often including both the count and contextual details about that quantity.
- 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_69c00825df388190a58742fa9b1aa33d |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022d2ed648190a5152c8668cbda02 |
completed | March 22, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69c01b2168508190b64b355cf50034ad |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:42 p.m.