Triple
T1193534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fontainebleau |
E25615
|
entity |
| Predicate | forestArea |
P25664
|
FINISHED |
| Object | about 250 square kilometres including surrounding communes |
—
|
LITERAL 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: about 250 square kilometres including surrounding communes | Statement: [Fontainebleau, forestArea, about 250 square kilometres including surrounding communes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: forestArea Context triple: [Fontainebleau, forestArea, about 250 square kilometres including surrounding communes]
-
A.
landArea
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
B.
areaTotalSquareKilometers
Indicates the total size of something measured in square kilometers.
-
C.
hasNationalForest
Indicates that a place or jurisdiction contains, includes, or is home to at least one designated national forest.
-
D.
landscapeType
Indicates the kind or category of natural terrain or scenery that characterizes a place or area.
-
E.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
- F. None of above. chosen
Provenance (4 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_69a49429f5ec8190a6a205eb0ae81e5e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd7743548190a70d3f3c7378aaa7 |
completed | March 1, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5d40a08190b7682d8ef8075421 |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bc49693c8190978ec63a5171d342 |
completed | March 1, 2026, 10:23 p.m. |
Created at: March 1, 2026, 7:46 p.m.