Triple
T5206404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sacsayhuamán |
E117520
|
entity |
| Predicate | estimatedArea |
P175
|
FINISHED |
| Object | over 3,000 hectares including surrounding archaeological park |
—
|
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: over 3,000 hectares including surrounding archaeological park | Statement: [Sacsayhuamán, estimatedArea, over 3,000 hectares including surrounding archaeological park]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedArea Context triple: [Sacsayhuamán, estimatedArea, over 3,000 hectares including surrounding archaeological park]
-
A.
areaApprox
Indicates that one entity’s area is approximately equal to the area of another entity.
-
B.
representedArea
Indicates that one entity serves as a representation or depiction of a particular area or region.
-
C.
hasAreaRange
Indicates that something’s area falls within a specified minimum-to-maximum range.
-
D.
area
chosen
Indicates that one entity has a measured two-dimensional extent or surface size quantified by another entity.
-
E.
effectiveArea
Indicates the portion of a surface or region that actually contributes to a specified effect, such as performance, interaction, or impact, within a given context.
- F. None of above.
Provenance (3 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_69bd4463dd3c81909966123f20b79d57 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7a4a7b7c8190af5a7149f8fe4f87 |
completed | March 20, 2026, 4:48 p.m. |
| PD | Predicate disambiguation | batch_69bd77bb4e8c819094b5ac7cf61512f9 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:47 p.m.