Triple
T3782591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | STM |
E85453
|
entity |
| Predicate | officialLanguage |
P236
|
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: [STM, officialLanguage, French]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: French Context triple: [STM, officialLanguage, 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 Swiss vehicle registration code for the canton of Fribourg.
-
C.
FR
FR is the IATA airline designator used to identify Ryanair flights.
-
D.
French Corner
French Corner is the English meaning of the name "Franschhoek," a South African town historically settled by French Huguenots.
-
E.
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.
- 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_69aed937fa8881908208ef3801060826 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee3db11108190aa81ee8ed22709fe |
completed | March 9, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4f04353a881908e612a10572eb8c5 |
completed | March 14, 2026, 5:21 a.m. |
Created at: March 9, 2026, 3:13 p.m.