Triple
T14939573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nina Banks |
E372486
|
entity |
| Predicate | hasWeddingEvent |
P110874
|
FINISHED |
| Object | wedding to Bryan MacKenzie |
—
|
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: wedding to Bryan MacKenzie | Statement: [Nina Banks, hasWeddingEvent, wedding to Bryan MacKenzie]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWeddingEvent Context triple: [Nina Banks, hasWeddingEvent, wedding to Bryan MacKenzie]
-
A.
hasCeremonyMonth
Indicates the month during which a particular ceremony takes place or is scheduled.
-
B.
hasWeddingSceneWith
Indicates that two entities appear together in a wedding scene within the same context or work.
-
C.
hasPublicCeremony
Indicates that a public ceremony is held or conducted in relation to the subject entity.
-
D.
hasGroom
Indicates that an entity has a groom, i.e., is associated with a male partner in a marriage or wedding relationship.
-
E.
associatedWithWeddingOf
chosen
Indicates a relationship where something is connected or related to the wedding event of specific individuals.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded64a2f24819099b21566756668a2 |
completed | April 15, 2026, 12:05 a.m. |
| PD | Predicate disambiguation | batch_69de9a588c2c8190b1245a1c406f447c |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:38 a.m.