Triple
T8848540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gascogne |
E210572
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Landes |
E453922
|
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: Landes | Statement: [Gascogne, contains, Landes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Landes Context triple: [Gascogne, contains, Landes]
-
A.
Landes
chosen
Landes is a department in southwestern France known for its vast Atlantic coastline, extensive pine forests, and popular surfing beaches.
-
B.
Ladonia
Ladonia is a small unincorporated community located within Russell County, Alabama.
-
C.
Rhegion
Rhegion was an important ancient Greek city located at the southern tip of Italy, strategically positioned on the Strait of Messina.
-
D.
Ille
Ille is a small river in northwestern France that flows through the city of Rennes and joins the Vilaine River.
-
E.
Valais
Valais is a mountainous canton in southwestern Switzerland known for its Alpine scenery, vineyards, and popular ski resorts such as Zermatt and Verbier.
- 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_69ca838967bc8190b46c3c80a2887ea4 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc60aa6db0819097c3257499200afc |
completed | April 1, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf89c6788881908d6f5c49434b556d |
completed | April 3, 2026, 9:35 a.m. |
Created at: March 30, 2026, 6:49 p.m.