Triple
T11635994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ubique |
E276519
|
entity |
| Predicate | hasFigurativeSense |
P93472
|
FINISHED |
| Object | present on all fronts |
—
|
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: present on all fronts | Statement: [Ubique, hasFigurativeSense, present on all fronts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFigurativeSense Context triple: [Ubique, hasFigurativeSense, present on all fronts]
-
A.
figurativeMeaning
chosen
Indicates that one entity is used in a non-literal, metaphorical, or symbolic sense to convey a meaning about another entity or concept.
-
B.
hasMetaphoricalForm
Indicates that one entity is expressed, represented, or understood through a metaphorical form or figurative expression involving another entity.
-
C.
hasLiteralMeaning
Indicates that one entity expresses the direct, explicit meaning or sense of another entity (such as a word, phrase, or symbol).
-
D.
hasSense
Indicates that an entity possesses or is associated with a particular sensory perception, meaning, or interpretation.
-
E.
hasIronicMeaning
Indicates that something conveys a meaning opposite to or incongruent with its literal expression, creating an ironic effect.
- 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_69d6aafa51148190ab84940694c00235 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a25d80208190b33e95db2e7cc276 |
completed | April 10, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69d85dd94bdc819091fa2ed33eb31624 |
completed | April 10, 2026, 2:18 a.m. |
Created at: April 8, 2026, 9:39 p.m.