Triple
T27794878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wagenmakersvallei |
E701178
|
entity |
| Predicate | hasApproximateEnglishMeaning |
P81437
|
FINISHED |
| Object | Wagon makers’ valley |
—
|
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: Wagon makers’ valley | Statement: [Wagenmakersvallei, hasApproximateEnglishMeaning, Wagon makers’ valley]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateEnglishMeaning Context triple: [Wagenmakersvallei, hasApproximateEnglishMeaning, Wagon makers’ valley]
-
A.
hasMeaningInOriginLanguage
Indicates that something (such as a word, phrase, or symbol) possesses a specific meaning in its original or source language.
-
B.
hasEnglishMeaningOfName
Indicates that one entity specifies the English meaning or interpretation of another entity’s name.
-
C.
hasTranslatedMeaning
Indicates that one entity expresses the meaning of another entity in a different language through translation.
-
D.
literalMeaningApproximation
chosen
Indicates that one entity expresses an approximate or rough literal meaning of another entity, rather than an exact or fully precise interpretation.
-
E.
hasLanguageSimilarTo
Indicates that one entity uses or is associated with a language that is similar or closely related to the language used or associated with 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_69ef6a50d8088190acbf3dfbb06d8091 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f739a638748190808e7a2930dce16e |
completed | May 3, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f732f2dc6c8190a4e86da98cc5eb05 |
completed | May 3, 2026, 11:35 a.m. |
Created at: April 27, 2026, 5:30 p.m.