Triple
T1214108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mono County |
E26068
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Monitor Pass |
E58088
|
NE 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: Monitor Pass | Statement: [Mono County, contains, Monitor Pass]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monitor Pass Context triple: [Mono County, contains, Monitor Pass]
-
A.
Monitor Pass
chosen
Monitor Pass is a high mountain roadway in California’s Sierra Nevada known for its scenic views and seasonal closures due to heavy snowfall.
-
B.
CSMonitor
CSMonitor is the abbreviated name for The Christian Science Monitor, an international news organization known for in-depth, balanced journalism.
-
C.
SmartScreen
SmartScreen is a Microsoft security technology that helps protect users by blocking malicious websites, downloads, and potentially unwanted applications in browsers like Microsoft Edge.
-
D.
Proscan
Proscan is a consumer electronics brand known for producing affordable televisions and related audio-visual equipment.
-
E.
ThousandEyes
ThousandEyes is a network intelligence and digital experience monitoring company, best known for its internet and cloud visibility platform that helps organizations troubleshoot and optimize application performance across complex, distributed environments.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a4948331fc8190b531ac9bec71c491 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be024e448190ba263a0cc5cc9cd5 |
completed | March 1, 2026, 10:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac831d216081909d36529fc4692361 |
completed | March 7, 2026, 7:57 p.m. |
Created at: March 1, 2026, 7:46 p.m.