Triple
T21757775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clara |
E537083
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Clara Bog |
—
|
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: Clara Bog | Statement: [Clara, near, Clara Bog]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clara Bog Context triple: [Clara, near, Clara Bog]
-
A.
Clara Bog
chosen
Clara Bog is a raised bog nature reserve in County Offaly, Ireland, renowned for its well-preserved peatland ecosystem and rich biodiversity.
-
B.
Gisclareny
Gisclareny is a small rural municipality in the Catalan Pyrenees of northeastern Spain, known for its mountainous landscapes and traditional farming character.
-
C.
Arleta
Arleta is a residential neighborhood in the San Fernando Valley region of Los Angeles, California.
-
D.
Claure
Claure is the surname of Marcelo Claure, a Bolivian-American businessman known for leading companies such as Sprint and SoftBank Group International.
-
E.
Naldehra
Naldehra is a serene hill station in the Indian state of Himachal Pradesh, known for its lush cedar forests, scenic mountain views, and historic golf course.
- 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01d8f64d881909cf10fd420153b09 |
completed | April 28, 2026, 2:38 a.m. |
Created at: April 16, 2026, 6:50 p.m.