Triple
T13581091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Capulet |
E324417
|
entity |
| Predicate | initialAttitudeTowardMarriageOfJuliet |
P69313
|
FINISHED |
| Object | cautious about early marriage |
—
|
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: cautious about early marriage | Statement: [Lord Capulet, initialAttitudeTowardMarriageOfJuliet, cautious about early marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: initialAttitudeTowardMarriageOfJuliet Context triple: [Lord Capulet, initialAttitudeTowardMarriageOfJuliet, cautious about early marriage]
-
A.
hasAttitudeTowardMarriage
Indicates that an entity holds a particular opinion, feeling, or stance regarding the institution or concept of marriage.
-
B.
attitudeTowardLove
Indicates an entity’s feelings, beliefs, or stance regarding the concept or experience of love.
-
C.
betrothalAge
Indicates the age at which a person becomes formally engaged to be married.
-
D.
initialAttitude
chosen
Indicates the starting stance, feeling, or disposition one entity holds toward another or toward a situation before any interaction or change occurs.
-
E.
asksToMarry
Indicates that one entity proposes marriage to another, requesting that they become spouses.
- 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.