Triple
T15036050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matthew Waterhouse |
E378478
|
entity |
| Predicate | fictionalCharacterPortrayedIn |
P33556
|
FINISHED |
| Object | TARDIS crew |
—
|
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: TARDIS crew | Statement: [Matthew Waterhouse, fictionalCharacterPortrayedIn, TARDIS crew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalCharacterPortrayedIn Context triple: [Matthew Waterhouse, fictionalCharacterPortrayedIn, TARDIS crew]
-
A.
fictionalCharacterAssociatedWith
Indicates that there is a notable connection or association between a fictional character and another entity, such as a work, creator, or universe.
-
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.
portraysFictionalEntity
chosen
Indicates that one entity depicts, represents, or plays the role of a fictional character or figure.
-
D.
characterPortrayedIs
Indicates that one entity serves as the fictional or dramatic role that is depicted or played by another entity.
-
E.
portrayedVia
Indicates that one entity is represented, depicted, or expressed through a particular medium, method, or channel.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded82b29948190acda49cbec3f927a |
completed | April 15, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69de9a67cbc481909c19c2de57de4eb7 |
completed | April 14, 2026, 7:50 p.m. |
Created at: April 10, 2026, 2:59 a.m.