Triple
T11578510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sango |
E274564
|
entity |
| Predicate | hasLoanwordsFrom |
P506
|
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: [Sango, hasLoanwordsFrom, French]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: French Context triple: [Sango, hasLoanwordsFrom, 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.
French
French is a common English-language surname of French origin borne by various notable individuals, including philanthropist Melinda Ann French (Melinda Gates).
-
C.
FR
FR is the vehicle registration code for the Freiburg im Breisgau district in the German state of Baden-Württemberg.
-
D.
FR
FR is the Swiss vehicle registration code for the canton of Fribourg.
-
E.
FR
FR is the ISO 3166-1 alpha-2 country code for France, a major European nation known for its rich culture, history, and global influence.
- 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_69d6aae5ac3c81908d2b0a3a665665b2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8904b46288190890ecafd6ceb0c3d |
completed | April 10, 2026, 5:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e714080a60819095205355776c8637 |
completed | April 21, 2026, 6:07 a.m. |
Created at: April 8, 2026, 9:38 p.m.