Triple
T37627936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villarepos |
E936259
|
entity |
| Predicate | hasForestLandUseShare |
P198026
|
FINISHED |
| Object | about 25 percent |
—
|
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 25 percent | Statement: [Villarepos, hasForestLandUseShare, about 25 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasForestLandUseShare Context triple: [Villarepos, hasForestLandUseShare, about 25 percent]
-
A.
forestCoverCharacteristic
Indicates a relationship where a forested area possesses a specific attribute or quality related to its tree or vegetation cover.
-
B.
hasLandUseSystem
Indicates that an entity is associated with or characterized by a particular system or pattern of land use.
-
C.
forestArea
Indicates the extent or size of land covered by forest within a given area or region.
-
D.
hasForestType
Indicates that an area or location is characterized by a specific type or classification of forest.
-
E.
hasRuralAreaShare
Indicates the proportion of an entity’s total area or population that is classified as rural.
- 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_69f76ed24820819081bafd36e9088701 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fec25f0fc48190b87ab1f9cd1eb0de |
completed | May 9, 2026, 5:13 a.m. |
| PD | Predicate disambiguation | batch_69fec079a770819098df7cc3049df954 |
completed | May 9, 2026, 5:04 a.m. |
| PDg | Predicate description generation | batch_69fec25e3d708190be27135c57b189a5 |
completed | May 9, 2026, 5:13 a.m. |
Created at: May 3, 2026, 4:18 p.m.