Triple
T32102056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barney Matthews |
E819875
|
entity |
| Predicate | hasRelationshipToHannibalLecter |
P175131
|
FINISHED |
| Object | custodian |
—
|
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: custodian | Statement: [Barney Matthews, hasRelationshipToHannibalLecter, custodian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipToHannibalLecter Context triple: [Barney Matthews, hasRelationshipToHannibalLecter, custodian]
-
A.
hasRelationshipToJackTorrance
Indicates that one entity has some form of relationship or connection to Jack Torrance.
-
B.
hasRelationshipToPerpetrator
Indicates that an entity has a specified type of relationship or connection to the perpetrator of an act or event.
-
C.
haveRelationshipWith
chosen
Indicates that one entity is in some form of defined relationship or association with another entity.
-
D.
hasRelationshipTypeWith Frank Drebin
Indicates that there exists a specific type of relationship between an entity and Frank Drebin.
-
E.
hasFamilialTieTo
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
- 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_69f34901106881908ea893ad504a08be |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a01d077dcec8190b05b24b5de313b15 |
completed | May 11, 2026, 12:50 p.m. |
| PD | Predicate disambiguation | batch_6a01cea0e37881909cb6888518c6d12f |
completed | May 11, 2026, 12:42 p.m. |
Created at: May 1, 2026, 12:26 a.m.