Triple
T20416056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berowne |
E500714
|
entity |
| Predicate | relationshipTypeWithRosaline |
P26473
|
FINISHED |
| Object | witty adversarial courtship |
—
|
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: witty adversarial courtship | Statement: [Berowne, relationshipTypeWithRosaline, witty adversarial courtship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithRosaline Context triple: [Berowne, relationshipTypeWithRosaline, witty adversarial courtship]
-
A.
relationshipToFlorentinoAriza
Indicates the nature of the relationship an entity has with Florentino Ariza.
-
B.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
C.
hasRomanticTensionWith
chosen
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
D.
relationshipToRose Castorini
Indicates the specific familial, romantic, or social relationship that one entity has to Rose Castorini.
-
E.
relationshipTypeWithTristan
Indicates the specific nature or category of the relationship that an entity has with Tristan.
- 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_69e0b4a935588190b9446a99b37ced44 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67a4437448190b07b6e6e3de5830f |
completed | April 20, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69e5766df0008190a73c4f613c29678f |
completed | April 20, 2026, 12:42 a.m. |
Created at: April 16, 2026, 11:30 a.m.