Triple
T18984127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CA 108 |
E464507
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Sierra Nevada |
—
|
NE NERFINISHED |
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: Sierra Nevada | Statement: [CA 108, locatedIn, Sierra Nevada]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sierra Nevada Context triple: [CA 108, locatedIn, Sierra Nevada]
-
A.
Sierra Nevada
Sierra Nevada is a prominent mountain range in southern Spain known for its high peaks, ski resorts, and inclusion in a national park.
-
B.
Sierra Nevada
chosen
Sierra Nevada is a major mountain range in the western United States known for its dramatic granite peaks, extensive forests, and iconic natural landmarks such as Yosemite National Park and Lake Tahoe.
-
C.
Sierra
Sierra is an off-price retail chain offering discounted brand-name and outdoor gear, operated as a sister brand to Marshalls under the TJX Companies portfolio.
-
D.
Sierra
Sierra is one of the central "Actives" in the TV series *Dollhouse*, known for her complex backstory and evolving sense of identity amid the show's mind-wiping technology.
-
E.
Sierra
Sierra is the Andean highland natural region of Peru, characterized by mountainous terrain, high plateaus, and a cool climate.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd008af48190a97ff1c6488edf1b |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d65ec0bc8190b878252b1b4c3620 |
completed | April 20, 2026, 7:31 a.m. |
Created at: April 10, 2026, 12:01 p.m.