Triple
T27451352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linda Susan Boreman |
E692449
|
entity |
| Predicate | activeYearsInPornography |
P168593
|
FINISHED |
| Object | early 1970s |
—
|
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 1970s | Statement: [Linda Susan Boreman, activeYearsInPornography, early 1970s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activeYearsInPornography Context triple: [Linda Susan Boreman, activeYearsInPornography, early 1970s]
-
A.
activeYearsInFilm
Indicates the span of years during which an entity was actively involved in film-related work or roles.
-
B.
activeYearsWith
Indicates the span of time during which an entity was actively engaged in a particular role, activity, or association with another entity.
-
C.
activeYearsInModeling
Indicates the span of time during which an entity was actively engaged in modeling.
-
D.
activeInYears
Indicates that an entity was active or operational during the specified years or year range.
-
E.
activeYearsPeak
Indicates the span of years during which an entity was at the height of its activity or prominence.
- 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_69ef5206c9248190b5975c2a7f9d229c |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f67595fa7c8190b6e9f7a8c700dd97 |
completed | May 2, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69f673c2f81c8190bf369226306eef09 |
completed | May 2, 2026, 9:59 p.m. |
| PDg | Predicate description generation | batch_69f674df80b08190adb7f7531083bbb1 |
completed | May 2, 2026, 10:04 p.m. |
Created at: April 27, 2026, 12:47 p.m.