Triple
T10077463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shafter |
E213804
|
entity |
| Predicate | distanceRelationToBakersfield |
P78976
|
FINISHED |
| Object | approximately 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 northwest | Statement: [Shafter, distanceRelationToBakersfield, approximately northwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceRelationToBakersfield Context triple: [Shafter, distanceRelationToBakersfield, approximately northwest]
-
A.
distanceToBakersfield
chosen
Indicates the spatial distance between a given location and the city of Bakersfield.
-
B.
distanceFromBarstow
Indicates the spatial distance between a given location and the reference point of Barstow.
-
C.
distanceFromLasVegas
Indicates the measured distance between a given place or object and the city of Las Vegas.
-
D.
distanceToFresno
Indicates the physical distance between a given location or entity and the city of Fresno.
-
E.
distanceFromSacramento
Indicates the measured distance between a given location and the city of Sacramento.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd02f47e08190bfeb641b202beecc |
completed | April 2, 2026, 2:10 a.m. |
| PD | Predicate disambiguation | batch_69cd4b97870481908f7a89df10d58a9e |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 8:59 p.m.