Triple
T34013993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Udon Thani |
E872189
|
entity |
| Predicate | hasNearbyBorderCrossingTo |
P27684
|
FINISHED |
| Object | Laos |
—
|
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: Laos | Statement: [Udon Thani, hasNearbyBorderCrossingTo, Laos]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyBorderCrossingTo Context triple: [Udon Thani, hasNearbyBorderCrossingTo, Laos]
-
A.
nearBorderCrossing
chosen
Indicates that an entity is located close to a border crossing point between two regions or countries.
-
B.
hasNearbyCrossBorderInfrastructure
Indicates that there exists infrastructure located near a border that connects or serves entities on both sides of that border.
-
C.
hasBorderCrossingActivity
Indicates that there is movement of people, goods, or services occurring across a shared border between two jurisdictions.
-
D.
hasBorderCrossingsType
Indicates that a border crossing is classified by a specific type or category of crossing (e.g., road, rail, pedestrian, maritime).
-
E.
hasBorderCrossingSide
Indicates that one side of a border crossing is associated with or located on a particular boundary or segment of that crossing.
- 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_69f349a08848819084b348d64c1879c3 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ffa9677be08190852c8ef6c2545fed |
completed | May 9, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69ffa6570e2c8190a9d7b37f12b91d9a |
completed | May 9, 2026, 9:25 p.m. |
Created at: May 1, 2026, 1:51 a.m.