Triple
T1618012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Río de la Plata |
E34763
|
entity |
| Predicate | widthNearBuenosAires |
P30480
|
FINISHED |
| Object | about 40 km |
—
|
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: about 40 km | Statement: [Río de la Plata, widthNearBuenosAires, about 40 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: widthNearBuenosAires Context triple: [Río de la Plata, widthNearBuenosAires, about 40 km]
-
A.
distanceFromBuenosAires
Indicates the measured distance between a given entity’s location and the city of Buenos Aires.
-
B.
distanceToSantiago_km
Indicates the physical distance, measured in kilometers, between a given location and Santiago.
-
C.
distanceFromSantiago
Indicates the spatial distance between a given entity and the location of Santiago.
-
D.
largestArgentineBase
Indicates that the subject is the largest Argentine base (e.g., by size, capacity, or another specified measure) among a relevant set of bases.
-
E.
enteredMontevideoDate
Indicates the date on which an entity arrived in or entered Montevideo.
- 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_69a885ffc5ec819091afa325d5f9611c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a93fef600c819080fe75c42c8e6dac |
completed | March 5, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69a907c52a548190b648a31ea306dd5b |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a93fedcb108190ad91f938d5eeaaa2 |
completed | March 5, 2026, 8:33 a.m. |
Created at: March 4, 2026, 7:28 p.m.