Triple
T29120296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Bogert |
E737160
|
entity |
| Predicate | employerFictionalIndustry |
P174708
|
FINISHED |
| Object | robotics |
—
|
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: robotics | Statement: [Peter Bogert, employerFictionalIndustry, robotics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerFictionalIndustry Context triple: [Peter Bogert, employerFictionalIndustry, robotics]
-
A.
possibleIndustry
Indicates a potential or likely industry with which an entity may be associated or classified.
-
B.
fictionalCorporation
Indicates that an entity is a corporation that exists only in fiction rather than in the real world.
-
C.
employerInPlot
Indicates that one entity serves as the employer of another within the context of a specific plot or storyline.
-
D.
targetCompanyIndustry
Indicates that a company operates within or is associated with a specified industry sector.
-
E.
employerInReality
Indicates that one entity is the actual, real-world employer of another entity, as opposed to a nominal, legal, or assumed employer.
- 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_69f077ed54e08190bb02a744e8121a66 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f6c5b7e46081909975b05f7298cc0e |
completed | May 3, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f23ae081909a52801266063a3c |
completed | May 3, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f6c49069e48190a3486b6254a6645b |
completed | May 3, 2026, 3:44 a.m. |
Created at: April 28, 2026, 11:25 a.m.