Triple
T16244553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julia Meade |
E394336
|
entity |
| Predicate | relationshipStatusInMissionImpossibleIII |
P44692
|
FINISHED |
| Object | engaged to Ethan Hunt |
—
|
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: engaged to Ethan Hunt | Statement: [Julia Meade, relationshipStatusInMissionImpossibleIII, engaged to Ethan Hunt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipStatusInMissionImpossibleIII Context triple: [Julia Meade, relationshipStatusInMissionImpossibleIII, engaged to Ethan Hunt]
-
A.
relationshipStatusDuringFilm
chosen
Indicates the type or state of a relationship between entities specifically during the time period in which a film takes place or is produced.
-
B.
relationshipStatusWithMichael
Indicates the type or state of the relationship that an entity currently has with Michael.
-
C.
relationshipStatusWithJackSparrow
Indicates the type or state of a subject’s personal relationship with Jack Sparrow.
-
D.
relationshipWithKateBeckett
Indicates that there exists a personal or professional relationship involving Kate Beckett and another entity.
-
E.
relationshipToMarkWatney
Indicates the specific type of personal or social relationship an entity has with Mark Watney.
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24560c6848190ae0e85ecb11a9264 |
completed | April 17, 2026, 2:36 p.m. |
| PD | Predicate disambiguation | batch_69e219ee6f6481909663b388dc99770a |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:04 a.m.