Triple
T4431303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huaral |
E95335
|
entity |
| Predicate | approximateDistanceInKilometresToLima |
P52231
|
FINISHED |
| Object | 80 |
—
|
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: 80 | Statement: [Huaral, approximateDistanceInKilometresToLima, 80]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateDistanceInKilometresToLima Context triple: [Huaral, approximateDistanceInKilometresToLima, 80]
-
A.
distanceFromLima
chosen
Indicates the measured distance between a given place or object and the city of Lima.
-
B.
distanceFromLaPazApproximate
Indicates an approximate measure of how far something is from La Paz, typically expressed as a rough distance rather than an exact value.
-
C.
distanceToSantiago_km
Indicates the physical distance, measured in kilometers, between a given location and Santiago.
-
D.
distanceFromBuenosAires
Indicates the measured distance between a given entity’s location and the city of Buenos Aires.
-
E.
distanceFromPotosiApproximate
Indicates an approximate measure of how far something is from Potosi.
- 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3556b71448190a3fab938853f8b87 |
completed | March 13, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69b34f6078cc8190831b89f404198cc5 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:31 p.m.