Triple
T16460863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nueva Segovia Department |
E399800
|
entity |
| Predicate | borderTypeWithHonduras |
P58557
|
FINISHED |
| Object | land border |
—
|
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: land border | Statement: [Nueva Segovia Department, borderTypeWithHonduras, land border]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderTypeWithHonduras Context triple: [Nueva Segovia Department, borderTypeWithHonduras, land border]
-
A.
borderTypeWithVenezuela
Indicates the specific type or nature of the border that an entity shares with Venezuela.
-
B.
borderTownGuatemala
Indicates that a town is located on or near the national border of Guatemala, functioning as a border town relative to that country.
-
C.
borderCountrySide
Indicates that one country shares a land border with the side or region of another country.
-
D.
countryBorderType
chosen
Indicates the type or nature of the border relationship that exists between two countries.
-
E.
borderTypeWithSpain
Indicates the type or nature of the border that an entity shares with Spain.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32d80e66c8190b2b3199efe9cfaa1 |
completed | April 18, 2026, 7:06 a.m. |
| PD | Predicate disambiguation | batch_69e227048d608190a4205eae3117629a |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:10 a.m.