Triple
T16224803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Carrera Lake |
E393817
|
entity |
| Predicate | hasSurfaceAreaInArgentina |
P122232
|
FINISHED |
| Object | ~880 km² |
—
|
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: ~880 km² | Statement: [General Carrera Lake, hasSurfaceAreaInArgentina, ~880 km²]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSurfaceAreaInArgentina Context triple: [General Carrera Lake, hasSurfaceAreaInArgentina, ~880 km²]
-
A.
unitArgentina
Indicates a relationship where an entity is associated with, belongs to, or is characterized as a unit related to Argentina.
-
B.
ianaArea
Indicates that one entity is associated with, or falls within, a specific IANA-defined geographic or administrative area represented by the other entity.
-
C.
areaInChile
Indicates that a place, region, or geographic area is located within the national territory of Chile.
-
D.
argentineSectorPartOf
Indicates that one entity is a sector or subdivision that forms a component part of a larger Argentine administrative or geographic unit.
-
E.
meanSurfaceArea_km2
Indicates the average surface area of an entity measured in square kilometers.
- 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_69d87f204df88190a8f88923decf9835 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e23d24f9688190b670cfeb73332293 |
completed | April 17, 2026, 2:01 p.m. |
| PD | Predicate disambiguation | batch_69e219e94a448190b73a4e6aa374eb4a |
completed | April 17, 2026, 11:30 a.m. |
| PDg | Predicate description generation | batch_69e21e55a2388190b29a045a8c608ba4 |
completed | April 17, 2026, 11:49 a.m. |
Created at: April 10, 2026, 5:03 a.m.