Triple
T7208437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CMP languages |
E148733
|
entity |
| Predicate | languageCount |
P14732
|
FINISHED |
| Object | dozens of individual 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: dozens of individual languages | Statement: [CMP languages, languageCount, dozens of individual languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageCount Context triple: [CMP languages, languageCount, dozens of individual languages]
-
A.
estimatedNumberOfLanguages
chosen
Indicates the approximate count of distinct languages associated with an entity, typically based on estimation rather than an exact measurement.
-
B.
currentNumberOfLanguages
Indicates the present count of distinct languages associated with or used by a given entity.
-
C.
hasApproximateNumberOfLanguages
Indicates that an entity is associated with a quantity representing an estimated or non-exact count of languages.
-
D.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
E.
numberOfScheduledLanguages
Indicates the total count of distinct languages that have been formally scheduled or planned for use in a given context or system.
- 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_69c687e8cf188190b5f3ecffd681f04e |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e96ae4dc8190b0b9e064ff968c10 |
completed | March 27, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69c6e75f84e481909e7866186ae80cff |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:52 p.m.