Triple
T35380041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avenue d'Auderghem / Oudergemlaan |
E1022618
|
entity |
| Predicate | hasBilingualToponym |
P24120
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Avenue d'Auderghem / Oudergemlaan, hasBilingualToponym, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBilingualToponym Context triple: [Avenue d'Auderghem / Oudergemlaan, hasBilingualToponym, yes]
-
A.
hasLanguageOfToponym
Indicates that a place name (toponym) is expressed in or associated with a particular language.
-
B.
bilingualName
chosen
Indicates that an entity has a name expressed in two different languages, linking the entity to its bilingual designation.
-
C.
isBilingualRegion
Indicates that a region officially uses two languages or has two predominant languages in regular use.
-
D.
hasToponymy
Indicates a relationship where one entity possesses or is associated with the system, study, or set of place names (toponyms) of another entity.
-
E.
hasToponymicForm
Indicates that one entity is a toponymic (place-name-based) form or variant derived from another entity.
- 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_69f76df28d8c819089f2c5799fe7d079 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7bbf906d8819099020e548dd56bc9 |
completed | May 3, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a2dcf88190a7c9e109e41267be |
completed | May 3, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:03 p.m.