Triple
T30794643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paraná (city) |
E784193
|
entity |
| Predicate | riverCrossingConnectionWith |
P33399
|
FINISHED |
| Object | Santa Fe (city, Argentina) |
—
|
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: Santa Fe (city, Argentina) | Statement: [Paraná (city), riverCrossingConnectionWith, Santa Fe (city, Argentina)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: riverCrossingConnectionWith Context triple: [Paraná (city), riverCrossingConnectionWith, Santa Fe (city, Argentina)]
-
A.
riverCrossingConnection
chosen
Indicates a connection between two locations that are linked by a route or structure specifically used to cross a river.
-
B.
hasRiverCrossingType
Indicates the type or nature of a river crossing associated with an entity (e.g., bridge, ford, ferry).
-
C.
riverCrossingFunction
Indicates a functional relationship where something or someone is transported or moved from one side of a river to the other.
-
D.
crossedByRiver
Indicates that a river passes across or through a specified area, feature, or route.
-
E.
hasRiverCrossingNearby
Indicates that there is a river crossing located in close proximity to the referenced entity or location.
- 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_69f224b2e2a48190b19aa43db9da5b67 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6db1f3ec48190a82e7d893d3c76ba |
completed | May 3, 2026, 5:20 a.m. |
| PD | Predicate disambiguation | batch_69f6d82adfa481908a5e196d2e18c73f |
completed | May 3, 2026, 5:07 a.m. |
Created at: April 29, 2026, 8:42 p.m.