Triple
T36732344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matthew–Luke–Mark |
E907373
|
entity |
| Predicate | literaryRelationshipClaim |
P99842
|
FINISHED |
| Object | Luke used Matthew |
—
|
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: Luke used Matthew | Statement: [Matthew–Luke–Mark, literaryRelationshipClaim, Luke used Matthew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryRelationshipClaim Context triple: [Matthew–Luke–Mark, literaryRelationshipClaim, Luke used Matthew]
-
A.
literaryRelationship
chosen
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
B.
fictionalRelationship
Indicates a relationship that exists only within a fictional or imagined context between entities.
-
C.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
-
D.
sexualRelationshipTo
Indicates that one entity has engaged in a sexual relationship or sexual activity with another entity.
-
E.
isRomanticLeadOf
Indicates that one entity serves as the primary romantic partner or love-interest counterpart to another entity within a narrative or story.
- 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_69f76e75aa6881909b844d00a3888ee5 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fce28d6c3081908bf76f5db63ecf68 |
completed | May 7, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69fce12d2f08819082134b5eb3db6a24 |
completed | May 7, 2026, 6:59 p.m. |
Created at: May 3, 2026, 4:12 p.m.