Triple
T34286804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baxter |
E879760
|
entity |
| Predicate | hasHumanRobotInteractionDesign |
P146301
|
FINISHED |
| Object | anthropomorphic face display |
—
|
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: anthropomorphic face display | Statement: [Baxter, hasHumanRobotInteractionDesign, anthropomorphic face display]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHumanRobotInteractionDesign Context triple: [Baxter, hasHumanRobotInteractionDesign, anthropomorphic face display]
-
A.
hasInteractiveTechnology
Indicates that an entity is equipped with or incorporates technology enabling user interaction or responsive engagement.
-
B.
hasInteraction
Indicates that there is some form of interaction or mutual action occurring between the related entities.
-
C.
hasInteractionMode
chosen
Indicates the way in which two entities engage or interact with each other, specifying the manner, channel, or pattern of their interaction.
-
D.
hasRobot
Indicates that one entity possesses, controls, or is associated with a robot.
-
E.
hasDesignInputFrom
Indicates that the design of one entity is based on, influenced by, or derived from input provided by another entity.
- 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_69f349b6df1c81908e5e5b6c2ab6409b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:57 a.m.