Triple
T16164515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanya Roberts as Stacey Sutton |
E392266
|
entity |
| Predicate | isHeiressInFiction |
P121647
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Tanya Roberts as Stacey Sutton, isHeiressInFiction, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isHeiressInFiction Context triple: [Tanya Roberts as Stacey Sutton, isHeiressInFiction, true]
-
A.
hasFamilyNameInFiction
Indicates that a fictional character is associated with a particular family name within a work of fiction.
-
B.
fictionalCharacter
Indicates that one entity is a fictional character that appears within the narrative world of another entity (such as a work, series, or franchise).
-
C.
isGivenNameOfFictionalCharacter
Indicates that a given name is the personal name borne by a fictional character.
-
D.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
-
E.
hasFictionalAuthor
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21e622ae481909f3cf25b38886d3a |
completed | April 17, 2026, 11:49 a.m. |
| PD | Predicate disambiguation | batch_69e1828abb608190a99d86bce1d77de2 |
completed | April 17, 2026, 12:44 a.m. |
| PDg | Predicate description generation | batch_69e18445155481909892b8aaa23cc159 |
completed | April 17, 2026, 12:52 a.m. |
Created at: April 10, 2026, 5:02 a.m.