Triple
T18519244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Basilio |
E452540
|
entity |
| Predicate | relationshipToBartolo |
P131988
|
FINISHED |
| Object | confidant and accomplice |
—
|
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: confidant and accomplice | Statement: [Don Basilio, relationshipToBartolo, confidant and accomplice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToBartolo Context triple: [Don Basilio, relationshipToBartolo, confidant and accomplice]
-
A.
relationshipToMiguel
Indicates the nature or type of relationship that one entity has with Miguel.
-
B.
relationshipToBaronOchs
Indicates the specific type of relationship or connection an entity has to Baron Ochs.
-
C.
relationshipToCarmen
Indicates the specific type of personal or social relationship an entity has with Carmen.
-
D.
relationshipToAntonioVillalta
Indicates the nature of the relationship or connection that an entity has to Antonio Villalta.
-
E.
relationshipWithKat Barton
Indicates the existence or nature of a relationship that an entity has with Kat Barton.
- 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_69d8d386df84819092355ebb260d848e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5338ce7e481908ee69ffe4f30d5a4 |
completed | April 19, 2026, 7:57 p.m. |
| PD | Predicate disambiguation | batch_69e469e0025c81908f16ed4f922674af |
completed | April 19, 2026, 5:36 a.m. |
| PDg | Predicate description generation | batch_69e46d2b93bc8190a6070018d7046547 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 11:36 a.m.