Triple
T2818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Pennsylvania |
E53
|
entity |
| Predicate | mottoTranslation |
P42
|
FINISHED |
| Object | Laws without morals are useless |
—
|
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: Laws without morals are useless | Statement: [University of Pennsylvania, mottoTranslation, Laws without morals are useless]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mottoTranslation Context triple: [University of Pennsylvania, mottoTranslation, Laws without morals are useless]
-
A.
motto
chosen
Indicates that one entity serves as the guiding phrase, slogan, or maxim associated with another entity.
-
B.
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.
-
C.
mission
Indicates that an entity is assigned or engaged in a specific task, operation, or purpose-directed undertaking.
-
D.
symbolizes
Indicates that one entity stands for, represents, or is used as a sign for another entity, concept, or idea.
-
E.
officialName
Indicates the formally recognized name assigned to an entity by an authoritative body or source.
- 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_69a22e0d37588190897cf37a323013f5 |
completed | Feb. 27, 2026, 11:51 p.m. |
| NER | Named-entity recognition | batch_69a2346846608190b6b40d31f1dbd685 |
completed | Feb. 28, 2026, 12:18 a.m. |
| PD | Predicate disambiguation | batch_69a233c396ec8190986608d07fb251d4 |
completed | Feb. 28, 2026, 12:16 a.m. |
Created at: Feb. 27, 2026, 11:55 p.m.