Triple
T30051637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Booker |
E763616
|
entity |
| Predicate | characterEmployer |
P93486
|
FINISHED |
| Object | corporate security firm |
—
|
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: corporate security firm | Statement: [Booker, characterEmployer, corporate security firm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterEmployer Context triple: [Booker, characterEmployer, corporate security firm]
-
A.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
B.
employerOfNotablePerson
Indicates that an entity serves or has served as the employer of a person who is considered notable.
-
C.
employerInPlot
chosen
Indicates that one entity serves as the employer of another within the context of a specific plot or storyline.
-
D.
formerEmployer
Indicates that one entity previously employed the other but no longer does so.
-
E.
parentEmployer
Indicates that one organization is the direct or higher-level employer of another organization or entity.
- 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_69f224716378819087a722e487832b70 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fdb31800508190beec15adb9bbd292 |
completed | May 8, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_69fdb19c381c8190bafb2f565da097f1 |
completed | May 8, 2026, 9:49 a.m. |
Created at: April 29, 2026, 6:55 p.m.