Triple
T11635993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ubique |
E276519
|
entity |
| Predicate | hasLiteralSense |
P3918
|
FINISHED |
| Object | in every place |
—
|
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: in every place | Statement: [Ubique, hasLiteralSense, in every place]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLiteralSense Context triple: [Ubique, hasLiteralSense, in every place]
-
A.
hasLiteralMeaning
chosen
Indicates that one entity expresses the direct, explicit meaning or sense of another entity (such as a word, phrase, or symbol).
-
B.
hasSense
Indicates that an entity possesses or is associated with a particular sensory perception, meaning, or interpretation.
-
C.
hasLinguisticDataType
Indicates that something is associated with or characterized by a specific type or category of linguistic data.
-
D.
hasSemantics
Indicates that one entity carries or encodes the meaning, interpretation, or semantic content associated with another entity.
-
E.
hasLinguisticElement
Indicates that one entity includes, is associated with, or is characterized by a particular linguistic component such as a word, phrase, symbol, or other language element.
- 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.