Triple
T29596740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony |
E754319
|
entity |
| Predicate | belongsToFictionalTechnologyClass |
P117060
|
FINISHED |
| Object | domestic service robot |
—
|
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: domestic service robot | Statement: [Tony, belongsToFictionalTechnologyClass, domestic service robot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToFictionalTechnologyClass Context triple: [Tony, belongsToFictionalTechnologyClass, domestic service robot]
-
A.
featuresFictionalTechnology
Indicates that an entity includes, depicts, or makes use of imagined or speculative technology that does not exist in reality.
-
B.
hasFictionalType
chosen
Indicates that an entity is associated with or classified under a particular type or category that is fictional rather than real.
-
C.
inUniverseTechnologyOf
Indicates that a technology exists within, and is part of, the fictional universe or setting associated with a given entity.
-
D.
hasFictionalUniverseElement
Indicates that one entity is a component, feature, or constituent part of the fictional universe represented by the other entity.
-
E.
hasFictionalSpecialization
Indicates that an entity’s area of focus, expertise, or role is within a fictional or imaginative domain rather than a real-world specialization.
- 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_69f0ef84e5d08190a0df17f5930ceed3 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69fd864235b481908738dbb69556bc62 |
completed | May 8, 2026, 6:44 a.m. |
| PD | Predicate disambiguation | batch_69fd8373b6bc819091c554f29ee17fec |
completed | May 8, 2026, 6:32 a.m. |
Created at: April 28, 2026, 6:18 p.m.