Triple
T20592395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sidney Greenbush |
E505961
|
entity |
| Predicate | workedAsChildIn |
P17879
|
FINISHED |
| Object | American television industry |
—
|
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: American television industry | Statement: [Sidney Greenbush, workedAsChildIn, American television industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workedAsChildIn Context triple: [Sidney Greenbush, workedAsChildIn, American television industry]
-
A.
workedPrimarilyOn
Indicates that an entity devoted the majority of its work, effort, or activity to a particular project, field, or subject.
-
B.
workedAs
Indicates that an entity held a particular job, role, or position, performing work in that capacity.
-
C.
spentChildhoodIn
Indicates that a person or entity spent the majority or formative years of their childhood in a particular place or location.
-
D.
workedUnder
Indicates that one entity was hierarchically subordinate to and performed work under the supervision or authority of another entity.
-
E.
hasWorkedIn
chosen
Indicates that a person has been employed or has performed work within a particular organization, location, or domain for some period of time.
- 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a97d63cc8190853e052d5930470d |
completed | April 20, 2026, 10:32 p.m. |
| PD | Predicate disambiguation | batch_69e59fffe1748190825e4eaa90340631 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:40 a.m.