Triple
T12326716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Il tabarro |
E293846
|
entity |
| Predicate | hasJealousHusbandCharacter |
P104499
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Il tabarro, hasJealousHusbandCharacter, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasJealousHusbandCharacter Context triple: [Il tabarro, hasJealousHusbandCharacter, yes]
-
A.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
-
B.
wasCheatedOnBy
Indicates that one entity was the victim of infidelity committed by another entity in a romantic or committed relationship.
-
C.
hasMarriagePlot
Indicates that the work’s narrative centrally involves courtship, romantic relationships, or the progression toward marriage as a key plot element.
-
D.
hasConcubineFrom
Indicates that a person has a concubine whose origin or affiliation is from a specified place or source.
-
E.
hasAffairWith
Indicates that one entity is engaged in a secret or illicit romantic or sexual relationship with another entity, typically outside a committed partnership.
- 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_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f621570819091ee1db2609233ea |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ec5be788190b82d2edc6a0f1095 |
completed | April 10, 2026, 6:17 p.m. |
| PDg | Predicate description generation | batch_69d93f607a88819089e89fd263ae9937 |
completed | April 10, 2026, 6:20 p.m. |
Created at: April 8, 2026, 9:53 p.m.