Triple
T11505659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apology against Jerome |
E272775
|
entity |
| Predicate | literaryRelationship |
P99842
|
FINISHED |
| Object | polemical exchange with Jerome |
—
|
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: polemical exchange with Jerome | Statement: [Apology against Jerome, literaryRelationship, polemical exchange with Jerome]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryRelationship Context triple: [Apology against Jerome, literaryRelationship, polemical exchange with Jerome]
-
A.
fictionalRelationship
Indicates a relationship that exists only within a fictional or imagined context between entities.
-
B.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
-
C.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
D.
spouseOrLover
Indicates a romantic partnership between two entities, whether formalized as a spouse or existing as a lover.
-
E.
partnerInLiteraryLifeOf
Indicates a relationship where one entity is a significant companion or collaborator in another entity’s literary career or creative writing life.
- 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_69d6aae2c3748190bed2ea50dfb160dc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d86db2f4b08190801de1b773932f59 |
completed | April 10, 2026, 3:25 a.m. |
| PD | Predicate disambiguation | batch_69d80876e5f0819088cff2e72f773cf6 |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:36 p.m.