Triple
T12261069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Dark Mirror |
E292224
|
entity |
| Predicate | hasTwinCharacters |
P104099
|
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: [The Dark Mirror, hasTwinCharacters, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTwinCharacters Context triple: [The Dark Mirror, hasTwinCharacters, true]
-
A.
hasTwin
Indicates that one entity is a twin of another, sharing the same birth event or time with a sibling.
-
B.
hasTwinFeature
Indicates that two entities share an identical or nearly identical feature, characteristic, or component, as if they are twins in that respect.
-
C.
hasTwinStructureWith
Indicates that two entities share an identical or nearly identical structural form, typically as corresponding or mirrored counterparts.
-
D.
isTwinWith
Indicates that two entities are twins, sharing the same birth parents and being born at (or very near) the same time.
-
E.
hasTwinActors
Indicates that two or more actors share a twin relationship, typically portraying twin characters or being treated as twins within a given context.
- 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_69d6ab6856488190b5d31178d5015f8e |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d9380a5e78819086bd4dfe9a83d1f5 |
completed | April 10, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69d91c4a66cc819083ce6fcaf5042af6 |
completed | April 10, 2026, 3:50 p.m. |
| PDg | Predicate description generation | batch_69d93805cee08190a532ebcf5908e617 |
completed | April 10, 2026, 5:48 p.m. |
Created at: April 8, 2026, 9:52 p.m.