Triple
T3083110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munsee language |
E64303
|
entity |
| Predicate | numberOfFluentSpeakers |
P1246
|
FINISHED |
| Object | very few |
—
|
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: very few | Statement: [Munsee language, numberOfFluentSpeakers, very few]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFluentSpeakers Context triple: [Munsee language, numberOfFluentSpeakers, very few]
-
A.
hasApproximateNativeSpeakers
chosen
Indicates that an entity is associated with an estimated or approximate number of people who speak it as their native language.
-
B.
secondLanguageSpeakers
Indicates that the referenced language is spoken as a second (non-native) language by the specified group or number of people.
-
C.
estimatedNumberOfLanguages
Indicates the approximate count of distinct languages associated with an entity, typically based on estimation rather than an exact measurement.
-
D.
rankedByNumberOfNativeSpeakers
Indicates that entities are ordered or classified according to how many native speakers they have.
-
E.
hasHighProportionOfSpeakersOf
Indicates that a subject entity has a relatively large share of its population or members who speak a specified language.
- 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_69ad857bb4c88190a4cf27893fcabed8 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada1e877008190aacbd6f1357bdb9b |
completed | March 8, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69ad9debb6308190be28378ae1fc98af |
completed | March 8, 2026, 4:03 p.m. |
Created at: March 8, 2026, 3:03 p.m.