Triple
T4600586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shoshone, California |
E100308
|
entity |
| Predicate | distanceToFurnaceCreek |
P57375
|
FINISHED |
| Object | approximately 60 miles |
—
|
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 60 miles | Statement: [Shoshone, California, distanceToFurnaceCreek, approximately 60 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToFurnaceCreek Context triple: [Shoshone, California, distanceToFurnaceCreek, approximately 60 miles]
-
A.
distanceFromBarstow
Indicates the spatial distance between a given location and the reference point of Barstow.
-
B.
distanceToWinnemucca
Indicates the spatial distance between a given entity’s location and the location of Winnemucca.
-
C.
distanceFromSocorro
Indicates the spatial distance between an entity and the reference location Socorro.
-
D.
distanceFromReno
Indicates the spatial distance between a given entity and the location Reno.
-
E.
distanceToFlagstaff
Indicates the measured distance between a given entity’s location and the location of Flagstaff.
- 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_69bd43cbc014819098b45f435908f88a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5971f448819090f6e76c7d3ffc2d |
completed | March 20, 2026, 2:28 p.m. |
| PD | Predicate disambiguation | batch_69bd522c811c81909aae4feadae33174 |
completed | March 20, 2026, 1:57 p.m. |
| PDg | Predicate description generation | batch_69bd56b5f4648190834eafa666d53caa |
completed | March 20, 2026, 2:16 p.m. |
Created at: March 20, 2026, 1:11 p.m.