Triple
T23609639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Becky Williams |
E583000
|
entity |
| Predicate | hasCloseWorkingRelationshipWith |
P107898
|
FINISHED |
| Object | Lucious Lyon |
—
|
NE NERFINISHED |
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: Lucious Lyon | Statement: [Becky Williams, hasCloseWorkingRelationshipWith, Lucious Lyon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCloseWorkingRelationshipWith Context triple: [Becky Williams, hasCloseWorkingRelationshipWith, Lucious Lyon]
-
A.
worksInCloseRelationshipWith
chosen
Indicates a collaborative professional relationship in which two or more entities work together closely and interact frequently to achieve shared goals.
-
B.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
-
C.
closelyInvolvedWith
Indicates a relationship in which one entity is deeply and actively engaged with another’s activities, decisions, or affairs.
-
D.
hasAllegedRelationshipWith
Indicates a claimed or suspected relationship between two entities that is not confirmed as factual.
-
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_69e248faa2788190abb1581742daa6aa |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b0f399cc8190a18d94b60fdca042 |
completed | April 29, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69f118d0e0588190a86527a7747c5427 |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:44 p.m.