Triple
T7723218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lorrain language |
E175064
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Gaumais |
E506576
|
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: Gaumais | Statement: [Lorrain language, hasDialect, Gaumais]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gaumais Context triple: [Lorrain language, hasDialect, Gaumais]
-
A.
Gaumais
chosen
Gaumais is a regional Romance dialect spoken in the Gaume area of southern Belgium, closely related to other Lorrain varieties.
-
B.
Mauzy
Mauzy is the surname of American actress Mackenzie Mauzy, known for her roles in television and film.
-
C.
Gaume
Gaume is a culturally distinct region in southern Belgium known for its milder microclimate, French-speaking population, and characteristic rural landscapes.
-
D.
Gamasa
Gamasa is a coastal city in Egypt’s Dakahlia Governorate, known for its Mediterranean shoreline and role as a regional urban center.
-
E.
Gardein
Gardein is a plant-based food brand known for its wide range of meatless products such as chicken, beef, and fish alternatives made from soy, wheat, and pea proteins.
- 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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702f39fa48190b7b8a09446b5cf78 |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b51faa348190b4fa0b5a307c83db |
completed | March 29, 2026, 5:14 a.m. |
Created at: March 27, 2026, 4:05 p.m.