Triple
T4152759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miskito |
E89944
|
entity |
| Predicate | hasSpeakerPopulationRange |
P36744
|
FINISHED |
| Object | hundreds of thousands |
—
|
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: hundreds of thousands | Statement: [Miskito, hasSpeakerPopulationRange, hundreds of thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpeakerPopulationRange Context triple: [Miskito, hasSpeakerPopulationRange, hundreds of thousands]
-
A.
haveSpeakerPopulation
chosen
Indicates that an entity has a specified number or population size of people who speak a particular language.
-
B.
hasAudienceSize
Indicates the relationship between an entity and the number of people or size of group that receives, views, or engages with it.
-
C.
hasSpeakerType
Indicates that an entity functions in a particular role or category as a speaker (e.g., narrator, character, announcer) within a given context.
-
D.
ageRange
Indicates the span of ages within which an entity or relationship is considered valid or applicable.
-
E.
typicalNumberOfVoices
Indicates the usual or characteristic number of distinct voices or parts involved in performing or realizing something (such as a musical work or texture).
- 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_69aed95a59a881909b26e70b42c6811a |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af033ef6648190adde17f943d89c78 |
completed | March 9, 2026, 5:28 p.m. |
| PD | Predicate disambiguation | batch_69af018c101081909070da5b11e5eb3d |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:44 p.m.