Triple
T3919815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States ten-dollar bill (reverse, historical designs) |
E88929
|
entity |
| Predicate | valueInUSD |
P52574
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [United States ten-dollar bill (reverse, historical designs), valueInUSD, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: valueInUSD Context triple: [United States ten-dollar bill (reverse, historical designs), valueInUSD, 10]
-
A.
valueInPhilippinePeso
Indicates the monetary value of something expressed in Philippine pesos as the unit of currency.
-
B.
valueInPula
Indicates that one entity represents the monetary value of another entity expressed in Botswana pula.
-
C.
monetaryValue
Indicates the amount of money associated with an entity, event, or transaction.
-
D.
valueInFrancs
Indicates that a specified monetary amount is expressed or measured in French francs.
-
E.
currency
Indicates that one entity serves as the medium of exchange or monetary unit used by another entity (such as a country, region, or system).
- 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_69aed955229881909e85e73ffab1d343 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef188b474819087680db42b04ecdd |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee75eedcc81908088ff4dbb8be56b |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aef18748648190b85e62f7796ff4b4 |
completed | March 9, 2026, 4:12 p.m. |
Created at: March 9, 2026, 3:22 p.m.