Triple
T14172952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin |
E351256
|
entity |
| Predicate | portraysVampiresAs |
P113104
|
FINISHED |
| Object | psychologically ambiguous rather than supernatural |
—
|
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: psychologically ambiguous rather than supernatural | Statement: [Martin, portraysVampiresAs, psychologically ambiguous rather than supernatural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysVampiresAs Context triple: [Martin, portraysVampiresAs, psychologically ambiguous rather than supernatural]
-
A.
hasVampireCharacter
Indicates that an entity includes or features at least one character who is a vampire.
-
B.
portraysReligionAs
Indicates that one entity represents, depicts, or characterizes a religion in a particular way.
-
C.
oftenDepictedAs
Indicates that one entity is frequently represented or portrayed in the form, appearance, or symbolism of another entity.
-
D.
portraysPersonAs
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
E.
portraysFictionalized
Indicates that one entity represents or depicts another entity in a fictionalized or altered manner, rather than as a strictly accurate portrayal.
- 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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61b5dcbc8190b0cfcce5e6c6d582 |
completed | April 14, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69de05baed64819096590e5618a3a8ed |
completed | April 14, 2026, 9:15 a.m. |
| PDg | Predicate description generation | batch_69de239a02e881909b0e2679487e4ab2 |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 10, 2026, 1:01 a.m.