Triple
T47716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haitian Creole |
E937
|
entity |
| Predicate | numberOfSpeakersApproximate |
P1247
|
FINISHED |
| Object | 12000000 |
—
|
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: 12000000 | Statement: [Haitian Creole, numberOfSpeakersApproximate, 12000000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSpeakersApproximate Context triple: [Haitian Creole, numberOfSpeakersApproximate, 12000000]
-
A.
hasApproximateTotalSpeakers
chosen
Indicates that an entity is associated with an estimated or roughly calculated number of total speakers, rather than an exact count.
-
B.
crewCountApproximate
Indicates that the relationship specifies an estimated or approximate number of crew members associated with an entity.
-
C.
hasApproximateNativeSpeakers
Indicates that an entity is associated with an estimated or approximate number of people who speak it as their native language.
-
D.
numberOfPersons
Indicates the total count of individual persons associated with or involved in a given entity, event, or context.
-
E.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24c1a5c14819088748317a3f262c8 |
completed | Feb. 28, 2026, 1:59 a.m. |
| PD | Predicate disambiguation | batch_69a24abe7cb481908d969e54032f6c75 |
completed | Feb. 28, 2026, 1:54 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.