Triple
T940773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese occupation of Singapore |
E20299
|
entity |
| Predicate | currencyNickname |
P21991
|
FINISHED |
| Object | banana money |
—
|
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: banana money | Statement: [Japanese occupation of Singapore, currencyNickname, banana money]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: currencyNickname Context triple: [Japanese occupation of Singapore, currencyNickname, banana money]
-
A.
currencyNameEnglish
Indicates the English-language name used to refer to a given currency.
-
B.
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).
-
C.
currencyType
Indicates the specific kind of monetary unit or currency associated with an entity or transaction.
-
D.
currencyOfDenomination
Indicates that one currency unit is a specific denomination or face value within another currency system.
-
E.
denominationName
Indicates the specific religious or organizational denomination to which an entity belongs or is associated.
- 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_69a493b0270c81909e6c9ce310f6aa55 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b38cc6888190b1d9043ec8fbcbc3 |
completed | March 1, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69a4b29c68f48190aecad10e351a99de |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b344f6f48190ba03ce593c94176b |
completed | March 1, 2026, 9:44 p.m. |
Created at: March 1, 2026, 7:40 p.m.