Triple
T13581092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Capulet |
E324417
|
entity |
| Predicate | laterAttitudeTowardMarriageOfJuliet |
P62195
|
FINISHED |
| Object | insistent on her marriage to Paris |
—
|
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: insistent on her marriage to Paris | Statement: [Lord Capulet, laterAttitudeTowardMarriageOfJuliet, insistent on her marriage to Paris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterAttitudeTowardMarriageOfJuliet Context triple: [Lord Capulet, laterAttitudeTowardMarriageOfJuliet, insistent on her marriage to Paris]
-
A.
hasAttitudeTowardMarriage
chosen
Indicates that an entity holds a particular opinion, feeling, or stance regarding the institution or concept of marriage.
-
B.
asksToMarry
Indicates that one entity proposes marriage to another, requesting that they become spouses.
-
C.
attitudeTowardLove
Indicates an entity’s feelings, beliefs, or stance regarding the concept or experience of love.
-
D.
acceptsMarriageTo
Indicates that one entity formally agrees to enter into a marriage with another entity.
-
E.
rivalForMarriageOf
Indicates that one entity is a romantic competitor with another entity for the opportunity to marry a specific third entity.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb03052088190a2b68c106059828e |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae161a0481909f9d3f40ca4e0ac5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:48 p.m.