Triple
T721787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tunisian dinar |
E14631
|
entity |
| Predicate | languageOfName |
P15
|
FINISHED |
| Object | French |
E13984
|
NE 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: French | Statement: [Tunisian dinar, languageOfName, French]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: French Context triple: [Tunisian dinar, languageOfName, French]
-
A.
French
chosen
French is a Romance language that evolved from Latin and is now spoken worldwide as both a native and official language in many countries.
-
B.
FR
FR is the IATA airline designator used to identify Ryanair flights.
-
C.
French American
French Americans are U.S. residents or citizens of French ancestry, including both descendants of early French settlers and more recent immigrants from France.
-
D.
The French
The French is a renowned fine-dining restaurant in Manchester’s Midland Hotel, known for its modern British cuisine and historic, elegant setting.
-
E.
Belgian French
Belgian French is the regional variety of the French language spoken in Belgium, characterized by distinctive vocabulary, pronunciation, and some grammatical features influenced by local languages and culture.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a591124c8190842e7ef18b064198 |
completed | March 1, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a64a5a7e788190b5ad2505b68ca48d |
completed | March 3, 2026, 2:41 a.m. |
Created at: March 1, 2026, 7:37 p.m.