Triple
T330408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iberian Peninsula |
E6612
|
entity |
| Predicate | areaApprox |
P11494
|
FINISHED |
| Object | about 580000 square kilometers |
—
|
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 580000 square kilometers | Statement: [Iberian Peninsula, areaApprox, about 580000 square kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaApprox Context triple: [Iberian Peninsula, areaApprox, about 580000 square kilometers]
-
A.
area
Indicates that one entity has a measured two-dimensional extent or surface size quantified by another entity.
-
B.
metroArea
Indicates that one location is part of, or belongs to, a specified metropolitan area.
-
C.
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).
-
D.
areaPeakApprox
Indicates an approximate measurement or estimation of the peak area associated with an entity or event.
-
E.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
- 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_69a2e79434908190a9d5afe415153ad9 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eaaeb64881909c7ab9bca3378e2b |
completed | Feb. 28, 2026, 1:16 p.m. |
| PD | Predicate disambiguation | batch_69a2e94c6d8881908239d3788c018adf |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea09a5e881908b313cb37409a4f9 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.