Triple
T12064073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | California State Route 84 |
E287247
|
entity |
| Predicate | crossesCityLimit |
P103013
|
FINISHED |
| Object | Fremont–Newark city limit |
—
|
LITERAL 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: Fremont–Newark city limit | Statement: [California State Route 84, crossesCityLimit, Fremont–Newark city limit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crossesCityLimit Context triple: [California State Route 84, crossesCityLimit, Fremont–Newark city limit]
-
A.
crossesInCity
Indicates that one entity crosses or passes through another entity within the boundaries of a specified city.
-
B.
crossedByHighway
Indicates that a highway passes across or through the extent of a given entity or area.
-
C.
crossesStreet
Indicates that an entity moves from one side of a street to the other, traversing the street’s width.
-
D.
crossingOf
Indicates that one entity serves as the intersection or crossing point of two or more linear features, such as roads, paths, or tracks.
-
E.
passesCity
Indicates that a route, path, or journey goes through or traverses a particular city as part of its course.
- F. None of above. chosen
Provenance (4 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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902bda47c8190b94860b31df4a98c |
completed | April 10, 2026, 2:01 p.m. |
| PDg | Predicate description generation | batch_69d91006e14081909838412df082f794 |
completed | April 10, 2026, 2:58 p.m. |
Created at: April 8, 2026, 9:48 p.m.