Triple
T19502004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dry Chaco |
E487925
|
entity |
| Predicate | border |
P224
|
FINISHED |
| Object | Pampas |
—
|
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: Pampas | Statement: [Dry Chaco, border, Pampas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pampas Context triple: [Dry Chaco, border, Pampas]
-
A.
Pampas
chosen
The Pampas is a vast fertile lowland plain in South America, primarily in Argentina, known for its grasslands, agriculture, and cattle ranching.
-
B.
Pampas
Pampas is a town in central Peru that serves as the administrative and commercial hub of Tayacaja Province in the Huancavelica Region.
-
C.
Pampa
Pampa is a small city in the Texas Panhandle known historically for its role in the oil and gas industry and as a regional service and trade center.
-
D.
Pampa
Pampa was a pioneering 10th-century Kannada poet, celebrated as one of the “three gems” of classical Kannada literature and best known for his epic works like the Adipurana and Vikramarjuna Vijaya.
-
E.
Pampa
Pampa is a jet trainer aircraft used by the Argentine Air Force, known for its role in pilot training and light attack missions.
- 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.