Triple
T16021967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aline |
E388622
|
entity |
| Predicate | hasOrigin |
P26
|
FINISHED |
| Object | French language |
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 language | Statement: [Aline, hasOrigin, French language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: French language Context triple: [Aline, hasOrigin, French language]
-
A.
Franzese
Franzese is an Italian surname borne by various notable individuals in fields such as entertainment and organized crime.
-
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.
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.
-
D.
Louis (French)
Louis is the French given name corresponding to the name Ludwik in other languages.
-
E.
French Wikiversity
French Wikiversity is the French-language edition of Wikiversity, a Wikimedia project dedicated to free educational resources and collaborative learning.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18323fc0881908bab7126d9ccf67d |
completed | April 17, 2026, 12:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf2c6128819091d8f3710578834e |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:55 a.m.