Triple
T11719589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kutenai language |
E278589
|
entity |
| Predicate | hasAgeProfileOfSpeakers |
P76657
|
FINISHED |
| Object | primarily older adults |
—
|
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: primarily older adults | Statement: [Kutenai language, hasAgeProfileOfSpeakers, primarily older adults]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAgeProfileOfSpeakers Context triple: [Kutenai language, hasAgeProfileOfSpeakers, primarily older adults]
-
A.
primarySpeakersAgeGroup
chosen
Indicates the age range category to which the main or primary speakers in a context belong.
-
B.
hasHighProportionOfSpeakersOf
Indicates that a subject entity has a relatively large share of its population or members who speak a specified language.
-
C.
haveSpeakerPopulation
Indicates that an entity has a specified number or population size of people who speak a particular language.
-
D.
audienceComposition
Indicates the makeup or distribution of different groups or segments within an audience in relation to something.
-
E.
containsAge
Indicates that one entity includes or specifies the age value or age-related information of another 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_69d6aaff2ce88190b4a1e4b341ad5377 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4c26e4c8190ae30d906b4fd4221 |
completed | April 10, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69d88a7d483081909c2a101087515d74 |
completed | April 10, 2026, 5:28 a.m. |
Created at: April 8, 2026, 9:40 p.m.