Triple
T16169970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | counterpart theory |
E392407
|
entity |
| Predicate | involvesDistinction |
P36189
|
FINISHED |
| Object | between counterparts and duplicates |
—
|
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: between counterparts and duplicates | Statement: [counterpart theory, involvesDistinction, between counterparts and duplicates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesDistinction Context triple: [counterpart theory, involvesDistinction, between counterparts and duplicates]
-
A.
distinction
Indicates that one entity is recognized, treated, or classified as different or separate from another.
-
B.
introducesDistinction
chosen
Indicates that one entity establishes or makes clear a conceptual or categorical difference between two or more entities or ideas.
-
C.
aimsToDistinguish
Indicates an intention or effort by one entity to set itself or something else apart from others by highlighting differences or unique characteristics.
-
D.
uniformDistinction
Indicates that a clear and consistent difference is maintained between two or more entities within a given context.
-
E.
hasInclusiveExclusiveDistinction
Indicates that the relationship or system distinguishes between inclusive and exclusive interpretations of a given element (such as “we” including or excluding certain participants).
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21eb6de30819083af54b50ae5ae51 |
completed | April 17, 2026, 11:51 a.m. |
| PD | Predicate disambiguation | batch_69e219d642708190ba31a90dce76a210 |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:02 a.m.