Triple
T22375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of California |
E444
|
entity |
| Predicate | translationOfMotto |
P1683
|
FINISHED |
| Object | Let there be light |
—
|
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: Let there be light | Statement: [University of California, translationOfMotto, Let there be light]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: translationOfMotto Context triple: [University of California, translationOfMotto, Let there be light]
-
A.
mottoType
Indicates the specific category or kind of motto that characterizes the relationship between an entity and its motto.
-
B.
motto
Indicates that one entity serves as the guiding phrase, slogan, or maxim associated with another entity.
-
C.
officialLanguage
Indicates that a particular language has been formally designated by an authority as the official language used for government, legal, or administrative purposes in a given jurisdiction.
-
D.
honorificNickname
Indicates that one entity is referred to by a respectful or honorific nickname by another entity or in a given context.
-
E.
nativeLabel
Indicates the label or name of an entity expressed in its own native or original language.
- 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_69a243b4ac2c8190b93c303df797b7b2 |
completed | Feb. 28, 2026, 1:24 a.m. |
| NER | Named-entity recognition | batch_69a246e94ca881908f7a7d2c0b293033 |
completed | Feb. 28, 2026, 1:37 a.m. |
| PD | Predicate disambiguation | batch_69a24654724481909ba14b7f68d2a472 |
completed | Feb. 28, 2026, 1:35 a.m. |
| PDg | Predicate description generation | batch_69a246e7fac481909b0c500d4500650e |
completed | Feb. 28, 2026, 1:37 a.m. |
Created at: Feb. 28, 2026, 1:34 a.m.