Triple
T19502005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dry Chaco |
E487925
|
entity |
| Predicate | border |
P224
|
FINISHED |
| Object | Monte Desert |
—
|
NE NERFINISHED |
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: Monte Desert | Statement: [Dry Chaco, border, Monte Desert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Desert Context triple: [Dry Chaco, border, Monte Desert]
-
A.
Monte Desert
chosen
The Monte Desert is a vast arid ecoregion in western Argentina characterized by shrublands, extreme temperature variations, and unique drought-adapted flora and fauna.
-
B.
Monte Calvo
Monte Calvo is a prominent mountain peak in Italy’s Gargano region, known as the highest elevation on the Gargano Peninsula.
-
C.
Monte Mor
Monte Mor is a municipality in the state of São Paulo, Brazil, known for its role in the Campinas metropolitan region and its growing industrial and residential development.
-
D.
Monte Renoso
Monte Renoso is a prominent mountain in southern Corsica, France, known for its rugged terrain and scenic alpine landscapes.
-
E.
Dry Mountain
Dry Mountain is the tallest peak in the remote Last Chance Range of eastern California’s Mojave Desert.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6350dbae08190bea7fc3e3eb95c3c |
completed | April 20, 2026, 2:15 p.m. |
Created at: April 10, 2026, 1:40 p.m.