Triple
T36303364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Timer |
E893879
|
entity |
| Predicate | hasDeviceInFiction |
P199967
|
FINISHED |
| Object | TiMER countdown implant |
—
|
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: TiMER countdown implant | Statement: [Timer, hasDeviceInFiction, TiMER countdown implant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDeviceInFiction Context triple: [Timer, hasDeviceInFiction, TiMER countdown implant]
-
A.
hasFictionalDeviceCapability
Indicates that an entity possesses the ability or function associated with a particular fictional device.
-
B.
hasFictionalDocument
Indicates that one entity possesses, is associated with, or includes a document that is fictional or exists only within an imagined or narrative context.
-
C.
hasFictionalAndroid
Indicates that an entity possesses, is associated with, or features a fictional android.
-
D.
hasFictionalType
Indicates that an entity is associated with or classified under a particular type or category that is fictional rather than real.
-
E.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
- 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_69f76e4c1b248190b10667d0213537fe |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ff65987ff88190b09be64f7c0e1da9 |
completed | May 9, 2026, 4:49 p.m. |
| PD | Predicate disambiguation | batch_69ff6525b0548190bef7a9f009e00bb8 |
completed | May 9, 2026, 4:47 p.m. |
| PDg | Predicate description generation | batch_69ff659717708190bb56714d1b261063 |
completed | May 9, 2026, 4:49 p.m. |
Created at: May 3, 2026, 4:09 p.m.