Triple
T20848035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Banquet |
E513277
|
entity |
| Predicate | hasOriginalTitleMeaning |
P142084
|
FINISHED |
| Object | Banquet |
—
|
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: Banquet | Statement: [The Banquet, hasOriginalTitleMeaning, Banquet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOriginalTitleMeaning Context triple: [The Banquet, hasOriginalTitleMeaning, Banquet]
-
A.
hasOriginalTitleScript
Indicates that an entity’s original title is written or represented in a specific writing system or script.
-
B.
originallyTitleOf
Indicates that one title is the original title from which another work, edition, or localized title is derived.
-
C.
hasOriginalCountryTitle
Indicates that an entity is associated with the title it originally had in its country of origin.
-
D.
originalTitleName
Indicates that one entity is the original or primary title name associated with another entity.
-
E.
originalLanguageTitle
Indicates the title of a work as it appears in its original language of creation or publication.
- 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_69e0b4f4898081908209e58edb8f9c45 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c351300c8190a4ec2eb430f0c23d |
completed | April 21, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e5c9a593f481908beb457c29f1ce73 |
completed | April 20, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e5d53c4d6881909b4d0a716fa5ed4a |
completed | April 20, 2026, 7:26 a.m. |
Created at: April 16, 2026, 12:43 p.m.