Triple
T19561960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langrois |
E489474
|
entity |
| Predicate | feminineForm |
P17779
|
FINISHED |
| Object | Langroise |
—
|
NE NERFINISHED |
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: Langroise | Statement: [Langrois, feminineForm, Langroise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langroise Context triple: [Langrois, feminineForm, Langroise]
-
A.
Langrois
chosen
Langrois is the French term for an inhabitant or native of the town of Langres in northeastern France.
-
B.
Roucour
Roucour is an alternative name for Rocourt, a district of Liège in Belgium known for its residential character and historical ties to the former municipality of the same name.
-
C.
Rocoux
Rocoux is a district of the city of Liège in eastern Belgium, known historically as the site of the 1746 Battle of Rocoux during the War of the Austrian Succession.
-
D.
Landais
Landais is a regional variety of the Gascon Occitan language traditionally spoken in parts of southwestern France.
-
E.
Dagneux
Dagneux is a commune in eastern France’s Ain department, known for its residential character and proximity to the Lyon metropolitan area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63f74fdb08190852461b5d5c954ac |
completed | April 20, 2026, 3 p.m. |
Created at: April 10, 2026, 1:42 p.m.