Triple
T2790425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Campinas |
E61914
|
entity |
| Predicate | distanceToSãoPaulo |
P43610
|
FINISHED |
| Object | approximately 100 km northwest |
—
|
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: approximately 100 km northwest | Statement: [Campinas, distanceToSãoPaulo, approximately 100 km northwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToSãoPaulo Context triple: [Campinas, distanceToSãoPaulo, approximately 100 km northwest]
-
A.
distanceToSantiago_km
Indicates the physical distance, measured in kilometers, between a given location and Santiago.
-
B.
distanceFromBuenosAires
Indicates the measured distance between a given entity’s location and the city of Buenos Aires.
-
C.
distanceFromPotosiApproximate
Indicates an approximate measure of how far something is from Potosi.
-
D.
distanceFromSantiago
Indicates the spatial distance between a given entity and the location of Santiago.
-
E.
distanceToLosAngeles
Indicates the measured or calculated distance between a given entity’s location and the city of Los Angeles.
- 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_69ab4b7f51d881908768300ebd2fbdae |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdeea881481908d759c72798a50fb |
completed | March 7, 2026, 8:16 a.m. |
| PD | Predicate disambiguation | batch_69abdd025c948190a97dd961a9592bac |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abdee94c2081908e5075e87e70780a |
completed | March 7, 2026, 8:16 a.m. |
Created at: March 6, 2026, 9:58 p.m.