Triple
T35980273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LaSalle |
E1040542
|
entity |
| Predicate | borderingCountryAcrossWater |
P200999
|
FINISHED |
| Object | United States of America |
—
|
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 of America | Statement: [LaSalle, borderingCountryAcrossWater, United States of America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderingCountryAcrossWater Context triple: [LaSalle, borderingCountryAcrossWater, United States of America]
-
A.
borderingWaters
Indicates that a geographic area directly touches or is adjacent to a particular body of water.
-
B.
bordersCountryViaWaterway
Indicates that two countries share a boundary that is defined or connected by a waterway such as a river, canal, or strait.
-
C.
bordersStateAcrossSea
Indicates that one state is separated from another by a sea but still directly borders it across that body of water.
-
D.
hasLandBorderWithSea
Indicates that an entity’s land area directly borders or touches a sea along its coastline.
-
E.
borderingCountryAcrossLake
Indicates that two countries share a border that is defined or separated by a lake lying between them.
- 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_69f76e28293c8190ae3f4e2208b87117 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ffc083a54c8190ac80d05ee8d20a6b |
completed | May 9, 2026, 11:17 p.m. |
| PD | Predicate disambiguation | batch_69ffbfeb05b88190b4d50ce8124004d9 |
completed | May 9, 2026, 11:14 p.m. |
| PDg | Predicate description generation | batch_69ffc082a4e881908a92313d2c755afe |
completed | May 9, 2026, 11:17 p.m. |
Created at: May 3, 2026, 4:07 p.m.