Triple
T27858417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Love Train |
E704148
|
entity |
| Predicate | followsCareerWith |
P180218
|
FINISHED |
| Object | Frankie Goes to Hollywood |
—
|
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: Frankie Goes to Hollywood | Statement: [Love Train, followsCareerWith, Frankie Goes to Hollywood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: followsCareerWith Context triple: [Love Train, followsCareerWith, Frankie Goes to Hollywood]
-
A.
followsCareerFrom
Indicates that one entity pursues or traces the professional path or career trajectory originating from or modeled after another entity.
-
B.
isFollowedByInCareer
Indicates that one person’s professional role, position, or career stage comes directly after another’s in sequence.
-
C.
followsWork
Indicates that one work (such as a publication, version, or creative piece) comes directly after another in sequence or succession.
-
D.
followedByRoleInCareerOf
Indicates that one role or position directly succeeds another in the sequence of roles within a single entity’s career.
-
E.
associatedWithCareerOf
Indicates a relationship where something is connected or relevant to a person’s professional life, occupation, or career trajectory.
- F. None of above. chosen
Provenance (4 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_69ef840e614c8190a88cf9638c14a265 |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69f739a638748190808e7a2930dce16e |
completed | May 3, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f732f2dc6c8190a4e86da98cc5eb05 |
completed | May 3, 2026, 11:35 a.m. |
| PDg | Predicate description generation | batch_69f739a58b3c81908abc2b8738a65678 |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 27, 2026, 6:16 p.m.