Triple
T37627937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villarepos |
E936259
|
entity |
| Predicate | hasSettlementLandUseShare |
P202574
|
FINISHED |
| Object | about 10 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 10 percent | Statement: [Villarepos, hasSettlementLandUseShare, about 10 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSettlementLandUseShare Context triple: [Villarepos, hasSettlementLandUseShare, about 10 percent]
-
A.
hasForestLandUseShare
Indicates the proportion of a given area’s land that is used or designated as forest.
-
B.
hasLandUseCharacter
Indicates that one entity possesses or is associated with a particular type or pattern of land use.
-
C.
hasLandUseSystem
Indicates that an entity is associated with or characterized by a particular system or pattern of land use.
-
D.
hasRuralAreaShare
Indicates the proportion of an entity’s total area or population that is classified as rural.
-
E.
hasLikelyLandUse
Indicates that an area or parcel is associated with a predicted or most probable type of land use (e.g., residential, commercial, agricultural).
- 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_6a009273634c8190b5b9c87053b56760 |
completed | May 10, 2026, 2:13 p.m. |
| PD | Predicate disambiguation | batch_6a0092171230819096e4274dd97e0410 |
completed | May 10, 2026, 2:11 p.m. |
| PDg | Predicate description generation | batch_6a009272afc88190a041b7e16d851599 |
completed | May 10, 2026, 2:13 p.m. |
Created at: May 3, 2026, 4:18 p.m.