Triple
T25731823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Ilsanjo |
E645260
|
entity |
| Predicate | hasClosestCity |
P143636
|
FINISHED |
| Object | Santa Rosa, California |
—
|
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: Santa Rosa, California | Statement: [Lake Ilsanjo, hasClosestCity, Santa Rosa, California]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasClosestCity Context triple: [Lake Ilsanjo, hasClosestCity, Santa Rosa, California]
-
A.
hasNearbyUSCity
Indicates that one location has at least one city in the United States situated within a specified nearby distance.
-
B.
hasNearbyCityFunction
Indicates that one entity serves as a nearby urban center or city-like service hub for another entity.
-
C.
nearestCityTo
chosen
Indicates that one city is the closest in distance to a given location or entity compared to all other cities.
-
D.
hasNearbyCityArea
Indicates that one area is geographically close to or adjacent to a city area.
-
E.
nearestMajorCity
Indicates that one city is the closest significant urban center to another location or city compared to all other major cities.
- F. None of above.
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_69e77e85254081908d79ee4e8715f283 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f65f7731e4819099d5bd3d915ee266 |
completed | May 2, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f65c1f94ac8190bc6fbc7916fc0d82 |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 21, 2026, 11:16 p.m.