Triple
T1244110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quo Fata Ferunt |
E26724
|
entity |
| Predicate | literalComponentMeaning |
P3918
|
FINISHED |
| Object | Quo = whither / to where |
—
|
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: Quo = whither / to where | Statement: [Quo Fata Ferunt, literalComponentMeaning, Quo = whither / to where]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literalComponentMeaning Context triple: [Quo Fata Ferunt, literalComponentMeaning, Quo = whither / to where]
-
A.
meaningComponent
Indicates that one entity represents a semantic or conceptual component contributing to the overall meaning of another entity.
-
B.
hasLiteralMeaning
chosen
Indicates that one entity expresses the direct, explicit meaning or sense of another entity (such as a word, phrase, or symbol).
-
C.
sourceLanguageMeaning
Indicates that one entity expresses the meaning or sense of another entity in a particular source language.
-
D.
possibleMeaning
Indicates that something may plausibly represent, signify, or be interpreted as a particular meaning or sense.
-
E.
originalMeaning
Indicates that something retains or conveys its initial, intended sense or significance, as opposed to a later or altered interpretation.
- 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_69a4948689d08190b3a4a3f388c02148 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf636e208190a4d56806db61916c |
completed | March 1, 2026, 10:36 p.m. |
| PD | Predicate disambiguation | batch_69a4bb696a38819095845c84f0241287 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.