Triple
T1390256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arm2 detector |
E29937
|
entity |
| Predicate | hasSisterSubsystem |
P28223
|
FINISHED |
| Object | Arm1 detector |
—
|
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: Arm1 detector | Statement: [Arm2 detector, hasSisterSubsystem, Arm1 detector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSisterSubsystem Context triple: [Arm2 detector, hasSisterSubsystem, Arm1 detector]
-
A.
hasSisterChannel
Indicates that one media channel is related to another as its sister channel, typically under common ownership or branding.
-
B.
hasSubcomponent
Indicates that one entity is a constituent part or component of another, larger entity.
-
C.
hasSisterDistrict
Indicates that one district is designated as a sister district to another, typically reflecting a formal partnership or cooperative relationship between them.
-
D.
hasSisterSite
Indicates that one site is formally associated with another as a sister site, typically implying a parallel or closely related counterpart.
-
E.
hasSisterOrganization
Indicates that one organization is related to another as a sister organization, typically sharing a common parent, affiliation, or parallel status within the same overarching structure.
- 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c35e023c8190b45688796d90534b |
completed | March 1, 2026, 10:53 p.m. |
| PD | Predicate disambiguation | batch_69a4beffcf808190ab4cd0271257ce63 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c2f16ae081908c92792253f3eb4b |
completed | March 1, 2026, 10:51 p.m. |
Created at: March 1, 2026, 7:59 p.m.