Triple
T5984233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Sign Language |
E133186
|
entity |
| Predicate | hasApproximateNumberOfUsers |
P22398
|
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: [American Sign Language, hasApproximateNumberOfUsers, hundreds of thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateNumberOfUsers Context triple: [American Sign Language, hasApproximateNumberOfUsers, hundreds of thousands]
-
A.
employsApproximateNumberOfPeople
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
-
B.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
-
C.
userCount
chosen
Indicates the number of users associated with or involved in a given context or entity.
-
D.
hasApproximateVendorCount
Indicates that an entity is associated with an estimated or non-exact number of vendors.
-
E.
approximateAudienceSize
Indicates an estimated number of individuals or entities that are expected to be reached or affected in a given context.
- 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_69c0087010d081908bb8142342d63330 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04a6c4f2481909cdcf931331b3595 |
completed | March 22, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69c049de98648190962b14fd341c93da |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:04 p.m.