Triple
T27479325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franklin |
E693554
|
entity |
| Predicate | nameBearerFieldOfWork |
P162428
|
FINISHED |
| Object | industrial innovation |
—
|
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: industrial innovation | Statement: [Franklin, nameBearerFieldOfWork, industrial innovation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nameBearerFieldOfWork Context triple: [Franklin, nameBearerFieldOfWork, industrial innovation]
-
A.
nameBearerFieldOfWork
chosen
Indicates that the field of work is associated with or characterized by the person or entity whose name it bears.
-
B.
nameBearerType
Indicates the specific role or capacity in which an entity bears or carries a given name (e.g., as a person, place, organization, or other type of name bearer).
-
C.
namesakeField
Indicates that one entity serves as the namesake for a particular field, such that the field is named after that entity.
-
D.
nameBearerHonor
Indicates that an entity bears or carries a name as an honor or mark of respect bestowed by another entity.
-
E.
namedForWork
Indicates that one entity is named in honor of, or derived from the title of, a particular work (such as a book, film, artwork, or other creative production).
- 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_69ef5381f2648190a2392d0fab833095 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f62e470e1c819099cbecb791f35990 |
completed | May 2, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69f62c1762f881908c25e8f70ecd5041 |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 27, 2026, 12:58 p.m.