Triple
T964757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Continental Edison Company |
E20813
|
entity |
| Predicate | roleInCareerOf |
P19243
|
FINISHED |
| Object | early employer of Nikola Tesla |
—
|
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: early employer of Nikola Tesla | Statement: [Continental Edison Company, roleInCareerOf, early employer of Nikola Tesla]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInCareerOf Context triple: [Continental Edison Company, roleInCareerOf, early employer of Nikola Tesla]
-
A.
roleInIndustry
Indicates the specific function, position, or capacity an entity holds within a particular industry or sector.
-
B.
describesCareerOf
Indicates that one entity provides a description or characterization of the professional career of another entity.
-
C.
roleInvolves
Indicates that a particular role includes or requires participation in a specified activity, responsibility, or function.
-
D.
partOfCareer
chosen
Indicates that one entity represents a role, position, or period that forms a component or phase within another entity’s overall career.
-
E.
roleInText
Indicates that an entity participates in a text with a specific function or capacity (e.g., author, editor, character).
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b4303e5881909d101d11f9732c75 |
completed | March 1, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a42c1481908d940cbe0aefdd3b |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.