Triple
T29117473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | California State Route 905 |
E737085
|
entity |
| Predicate | connectsCountryToBorder |
P126842
|
FINISHED |
| Object | United States–Mexico border |
—
|
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: United States–Mexico border | Statement: [California State Route 905, connectsCountryToBorder, United States–Mexico border]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsCountryToBorder Context triple: [California State Route 905, connectsCountryToBorder, United States–Mexico border]
-
A.
connectsCountryBorder
Indicates that one entity forms a direct land or maritime border connection with a specified country.
-
B.
connectsToCountryBorder
chosen
Indicates that one entity is directly adjacent to and touches the border of a specified country.
-
C.
borderingCountryConnectivity
Indicates that two countries share a land or maritime border that allows direct movement or interaction between them.
-
D.
geographicallyBorders
Indicates that two geographic entities share a common boundary or border.
-
E.
connectsCapitalToBorder
Indicates a relationship where a capital city is linked or directly connected to a border location or boundary.
- 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_69f077ed54e08190bb02a744e8121a66 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69fd7e364a648190a1e9e1d9fc76e99e |
completed | May 8, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69fd7bb547608190a3b04dddbca6b8bc |
completed | May 8, 2026, 5:59 a.m. |
Created at: April 28, 2026, 11:23 a.m.