Triple
T17607154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Officer Anderson |
E428861
|
entity |
| Predicate | hasNotablePortrayal |
P20085
|
FINISHED |
| Object | Michael Biehn performance |
—
|
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: Michael Biehn performance | Statement: [Officer Anderson, hasNotablePortrayal, Michael Biehn performance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotablePortrayal Context triple: [Officer Anderson, hasNotablePortrayal, Michael Biehn performance]
-
A.
hasNotablePortrayerOccupation
Indicates that the occupation specified is a notable profession of a person who portrays the given entity (such as an actor playing a character).
-
B.
hasYoungPortrayalOf
Indicates that one entity is a portrayal or depiction of another entity specifically in their younger age or earlier life stage.
-
C.
notableDepictionBy
chosen
Indicates that an entity is significantly portrayed or represented by a particular creator, work, or medium.
-
D.
notablePortrayalPeriod
Indicates the time period during which a particular portrayal or depiction of something or someone is especially recognized or notable.
-
E.
hasPortrayedRole
Indicates that an entity has performed or depicted a specific role or character, typically in a work such as a film, play, or television show.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c4ccef08190aeaa88670364bd74 |
completed | April 19, 2026, 5:46 a.m. |
| PD | Predicate disambiguation | batch_69e3cdd7da34819099bc9481c5a79bab |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 5:51 a.m.