Triple
T26379934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crenshaw (mixtape) |
E660996
|
entity |
| Predicate | grossRevenueFrom$100Sales |
P160477
|
FINISHED |
| Object | 100000 |
—
|
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: 100000 | Statement: [Crenshaw (mixtape), grossRevenueFrom$100Sales, 100000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grossRevenueFrom$100Sales Context triple: [Crenshaw (mixtape), grossRevenueFrom$100Sales, 100000]
-
A.
revenue
Indicates the amount of income generated by an entity from its business activities or operations over a specified period.
-
B.
revenueLevel
Indicates the relative amount or tier of revenue associated with an entity or activity.
-
C.
revenueUse
Indicates how generated revenue is allocated, spent, or applied toward specific purposes or activities.
-
D.
hasRevenueUnit
Indicates that an entity’s revenue is measured, reported, or associated in terms of a specified unit (e.g., currency or measurement unit).
-
E.
totalGross
Indicates the overall amount of money generated in revenue, typically from all sources over a specified period or for a specific work or event.
- 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_69ee812a698881908d6a58265995fa39 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f610740cb4819086aa7efc63cf0a9a |
completed | May 2, 2026, 2:55 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69f6018ceb1c8190a6a5f84071659a96 |
completed | May 2, 2026, 1:52 p.m. |
Created at: April 26, 2026, 11:03 p.m.