Triple
T30824812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bechuanaland Protectorate pound |
E785025
|
entity |
| Predicate | usedLanguageOnNotesAndCoins |
P121037
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Bechuanaland Protectorate pound, usedLanguageOnNotesAndCoins, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedLanguageOnNotesAndCoins Context triple: [Bechuanaland Protectorate pound, usedLanguageOnNotesAndCoins, English]
-
A.
languageOnNotesAndCoins
chosen
Indicates the language that appears on a country’s official banknotes and coins.
-
B.
languageOnBanknotes
Indicates the language that is printed or used on a country's banknotes.
-
C.
languageOnCoins
Indicates the language that is inscribed or used on a set of coins.
-
D.
writingSystemUsedOnCoins
Indicates that the coins feature inscriptions or markings written in the specified writing system.
-
E.
languageOnStamp
Indicates that a particular language appears on or is used in the text or inscriptions printed on a stamp.
- 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_69f224b6642481909e8d701de2cd1a53 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f690f4e10c8190a3c68f9827c0f2a9 |
completed | May 3, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69f68b7d2794819092fef8a63f4f3de8 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 29, 2026, 8:44 p.m.