Triple
T357044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malaysian ringgit |
E7566
|
entity |
| Predicate | formerCoins |
P2872
|
FINISHED |
| Object | 1 sen |
—
|
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: 1 sen | Statement: [Malaysian ringgit, formerCoins, 1 sen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerCoins Context triple: [Malaysian ringgit, formerCoins, 1 sen]
-
A.
formerCurrency
Indicates that an entity was once used as a currency in a place or time period but is no longer in official use.
-
B.
coinDenomination
chosen
Indicates the specific monetary value assigned to a coin within a currency system.
-
C.
coinedIn
Indicates that something (typically a term, phrase, or name) was first created or introduced at a particular time or in a particular place.
-
D.
authorizedCoinMetal
Indicates that a particular metal is officially approved for use in minting a given coin.
-
E.
previousDenomination
Indicates that one entity was the earlier or former denomination (name, value, or classification) of another entity in a sequence of denominations.
- 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_69a2e7e696948190bebc966535995e45 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebaf0c9881909313f98818e7fa58 |
completed | Feb. 28, 2026, 1:20 p.m. |
| PD | Predicate disambiguation | batch_69a2e959ce948190a201c017eecb7c95 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.