Triple
T33198194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steffi Duna |
E849824
|
entity |
| Predicate | workedWithIndustry |
P17879
|
FINISHED |
| Object | American film industry |
—
|
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 film industry | Statement: [Steffi Duna, workedWithIndustry, American film industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workedWithIndustry Context triple: [Steffi Duna, workedWithIndustry, American film industry]
-
A.
workedAs
Indicates that an entity held a particular job, role, or position, performing work in that capacity.
-
B.
hasWorkedIn
chosen
Indicates that a person has been employed or has performed work within a particular organization, location, or domain for some period of time.
-
C.
workedWithLanguageIndustry
Indicates that an entity has performed work or professional activities within the language industry, such as translation, localization, interpreting, or related language services.
-
D.
workedAmong
Indicates that an individual carried out work or professional activities within a particular group, organization, or community.
-
E.
workedPrimarilyOn
Indicates that an entity devoted the majority of its work, effort, or activity to a particular project, field, or subject.
- 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_69f3495efedc8190843a5728089544b9 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6dd3cc0648190a275812d6711275a |
completed | May 3, 2026, 5:29 a.m. |
| PD | Predicate disambiguation | batch_69f6d82eaee081908f06a71546315aea |
completed | May 3, 2026, 5:07 a.m. |
Created at: May 1, 2026, 1:29 a.m.