Triple
T38350084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gabe Nevins |
E1041655
|
entity |
| Predicate | basedOnCareer |
P200360
|
FINISHED |
| Object | American independent cinema |
—
|
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 independent cinema | Statement: [Gabe Nevins, basedOnCareer, American independent cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnCareer Context triple: [Gabe Nevins, basedOnCareer, American independent cinema]
-
A.
basedOnCareerOf
Indicates that something (such as a work, character, or storyline) is derived from, inspired by, or modeled on the career or professional life of a particular person.
-
B.
basedOnProfession
Indicates that the relationship or action is determined or derived from a person’s profession or occupational role.
-
C.
isCareerBased
Indicates that something is determined, structured, or oriented around a person’s career or professional path.
-
D.
basedOnExperience
Indicates that something is determined, chosen, or formed according to prior experience or experiential knowledge.
-
E.
settingOfCareer
Indicates the primary environment, context, or domain in which a person’s career takes place or is situated.
- 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_69f76e2ad95481908c920c0e5c1c3e26 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff84202eb081908ae21a54a4414d68 |
completed | May 9, 2026, 6:59 p.m. |
| PD | Predicate disambiguation | batch_69ff833065e4819098579129d4ee17d3 |
completed | May 9, 2026, 6:55 p.m. |
| PDg | Predicate description generation | batch_69ff841f2f2081908d72d4f878c538a0 |
completed | May 9, 2026, 6:59 p.m. |
Created at: May 3, 2026, 4:30 p.m.