Triple
T94056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Havana |
E1890
|
entity |
| Predicate | hasMottoInEnglish |
P1683
|
FINISHED |
| Object | University and Humanity |
—
|
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: University and Humanity | Statement: [University of Havana, hasMottoInEnglish, University and Humanity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMottoInEnglish Context triple: [University of Havana, hasMottoInEnglish, University and Humanity]
-
A.
translationOfMotto
chosen
Indicates that one motto is a translation of another motto in a different language.
-
B.
mottoType
Indicates the specific category or kind of motto that characterizes the relationship between an entity and its motto.
-
C.
motto
Indicates that one entity serves as the guiding phrase, slogan, or maxim associated with another entity.
-
D.
formerMotto
Indicates that a motto was previously used by an entity but is no longer its current motto.
-
E.
hasEndonym
Indicates that an entity has a name or designation used by native speakers or within its own local language or community.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a2512ef600819084d3c627f0d534f4 |
completed | Feb. 28, 2026, 2:21 a.m. |
| PD | Predicate disambiguation | batch_69a24eb9a5ac8190b1d1300e8c4e3606 |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.