Triple
T38642385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Sound Studio |
E938632
|
entity |
| Predicate | approximateNumberOfHits |
P201574
|
FINISHED |
| Object | over 100 chart hits |
—
|
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: over 100 chart hits | Statement: [American Sound Studio, approximateNumberOfHits, over 100 chart hits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberOfHits Context triple: [American Sound Studio, approximateNumberOfHits, over 100 chart hits]
-
A.
approximateDocumentCount
Indicates an estimated number of documents associated with or contained by a given entity, rather than an exact count.
-
B.
mineCountApproximate
Indicates that the number of mines associated with an entity is estimated or roughly counted rather than known exactly.
-
C.
maskCountApproximate
Indicates that the number of masks involved is represented as an approximate (non-exact) count.
-
D.
approximateEstimation
Indicates an estimation relationship where one value or assessment is only roughly or closely, but not exactly, equal to another.
-
E.
approximateDropCount
Indicates an estimated number of times an item, object, or entity has been dropped, rather than an exact count.
- F. None of above. chosen
Provenance (4 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_69f76ed948ec81908ce7811608a8f359 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a00053d8004819097ad9cf6431a20a3 |
completed | May 10, 2026, 4:10 a.m. |
| PD | Predicate disambiguation | batch_6a0004b3a82c81908e2bf9a533a93eb6 |
completed | May 10, 2026, 4:08 a.m. |
| PDg | Predicate description generation | batch_6a00053cdcb08190980df73e235e9ad8 |
completed | May 10, 2026, 4:10 a.m. |
Created at: May 3, 2026, 4:32 p.m.