Triple
T20501332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sonnet 147 |
E503310
|
entity |
| Predicate | portraysBelovedAs |
P100368
|
FINISHED |
| Object | morally corrupt |
—
|
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: morally corrupt | Statement: [Sonnet 147, portraysBelovedAs, morally corrupt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysBelovedAs Context triple: [Sonnet 147, portraysBelovedAs, morally corrupt]
-
A.
portraysPersonAs
chosen
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
B.
portraysAtticusAs
Indicates that a subject represents or depicts Atticus in a particular manner or characterization.
-
C.
portraysRelationship
Indicates that one entity depicts, represents, or illustrates a relationship between other entities.
-
D.
hasFictionalBeloved
Indicates that an entity has a romantic partner or beloved who exists only as a fictional character.
-
E.
portraysCharacterRelationship
Indicates that one entity depicts or represents the relationship between characters in another 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_69e0b4b1e52c8190894281cf7e3283ab |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69cc1ea7081908d17c224b1670bd7 |
completed | April 20, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69e59fcdf6e08190a604204615dc56e6 |
completed | April 20, 2026, 3:38 a.m. |
Created at: April 16, 2026, 11:35 a.m.