Triple
T23016561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boise |
E573044
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | City of Trees |
—
|
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: City of Trees | Statement: [Boise, nickname, City of Trees]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Trees Context triple: [Boise, nickname, City of Trees]
-
A.
City of Trees
City of Trees is the popular nickname for Sendai, a major city in Japan renowned for its abundant greenery and tree-lined streets.
-
B.
City of Trees
City of Trees is a popular nickname for Sacramento, California, highlighting its extensive urban tree canopy and lush greenery.
-
C.
City of Trees
chosen
City of Trees is the nickname for Boise, Idaho, highlighting the city's abundant urban greenery and tree-lined landscape.
-
D.
City of Trees
City of Trees is the nickname of Burlingame, California, highlighting its abundant tree-lined streets and lush urban canopy.
-
E.
The City of Trees
The City of Trees is the nickname and motto of Takoma Park, Maryland, highlighting the community’s strong emphasis on urban forestry and abundant tree canopy.
- 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_69e245b764cc8190a51be76f1d9611e1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f183e59a1c8190b8048a399a4727cb |
completed | April 29, 2026, 4:07 a.m. |
Created at: April 17, 2026, 3:52 p.m.