Triple
T28585069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara Stanwyck as Lee Leander |
E723474
|
entity |
| Predicate | travelsWithOtherCharacter |
P37304
|
FINISHED |
| Object | accompanies John Sargent on trip to Indiana |
—
|
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: accompanies John Sargent on trip to Indiana | Statement: [Barbara Stanwyck as Lee Leander, travelsWithOtherCharacter, accompanies John Sargent on trip to Indiana]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelsWithOtherCharacter Context triple: [Barbara Stanwyck as Lee Leander, travelsWithOtherCharacter, accompanies John Sargent on trip to Indiana]
-
A.
collaboratesWithCharacter
Indicates that one character works together with another character toward a shared goal or activity.
-
B.
relatedCharacter
chosen
Indicates that one character has a specified relationship or association with another character.
-
C.
meetsFictionalCharacter
Indicates that one entity encounters or comes into contact with a fictional character.
-
D.
attendedByFictionalCharacter
Indicates that a fictional character is present at, participates in, or is an attendee of a particular event or gathering.
-
E.
hasFictionalCoStar
Indicates that one entity appears as a co-star alongside another entity within a fictional work or narrative.
- 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_69f01d7f92e481909847f5f3f3174a89 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f67257b0448190a13011af81c81449 |
completed | May 2, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69f66ec5bf508190ad088b89455252bd |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 28, 2026, 4:17 a.m.