Triple
T25083338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haiti–Dominican Republic border |
E628243
|
entity |
| Predicate | languageOnWestSide |
P168529
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Haiti–Dominican Republic border, languageOnWestSide, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOnWestSide Context triple: [Haiti–Dominican Republic border, languageOnWestSide, French]
-
A.
hasLanguageOnEasternSide
Indicates that a specified language is used or spoken on the eastern side of a given area, boundary, or region.
-
B.
languageOnBothSides
Indicates that the same language is used or present on both sides of a given relationship, boundary, or comparison.
-
C.
hasLanguageOnSouthernSide
Indicates that a specified language is used or present on the southern side of a given boundary, area, or entity.
-
D.
languageUsedInLocality
Indicates that a particular language is used or spoken within a specific locality or geographic area.
-
E.
languageOfSurroundingCulture
Indicates that one entity is the language predominantly used or characteristic of the surrounding culture associated with another entity.
- 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_69e2ff2e73f881909992bf3eda5c25cb |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f67595fa7c8190b6e9f7a8c700dd97 |
completed | May 2, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69f673c2f81c8190bf369226306eef09 |
completed | May 2, 2026, 9:59 p.m. |
| PDg | Predicate description generation | batch_69f674df80b08190adb7f7531083bbb1 |
completed | May 2, 2026, 10:04 p.m. |
Created at: April 18, 2026, 6:22 a.m.