Triple
T21321646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Rowan |
E525631
|
entity |
| Predicate | activeYearsInShowBusiness |
P18004
|
FINISHED |
| Object | 1940s–1980s |
—
|
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: 1940s–1980s | Statement: [Dan Rowan, activeYearsInShowBusiness, 1940s–1980s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activeYearsInShowBusiness Context triple: [Dan Rowan, activeYearsInShowBusiness, 1940s–1980s]
-
A.
activeYearsInFilm
Indicates the span of years during which an entity was actively involved in film-related work or roles.
-
B.
activeYearsInCareer
chosen
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
C.
activeInYears
Indicates that an entity was active or operational during the specified years or year range.
-
D.
yearsActiveInStory
Indicates the span of years during which the entity is active or participates within the context of the story.
-
E.
activeYearsInModeling
Indicates the span of time during which an entity was actively engaged in modeling.
- 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_69e0b51ad810819098c12392c8e55f6c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e77ed2640c8190a81b087e2c49c500 |
completed | April 21, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69e6161feea4819091d13bb003363279 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 4:39 p.m.