Triple
T34294640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Good Service Medal, Bronze |
E879988
|
entity |
| Predicate | reverseDesignLanguage |
P178860
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Good Service Medal, Bronze, reverseDesignLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reverseDesignLanguage Context triple: [Good Service Medal, Bronze, reverseDesignLanguage, English]
-
A.
reverseDesignTitle
Indicates that one design’s title is the reverse or inverse counterpart of another design’s title.
-
B.
reverseDesigns
Indicates that one entity creates or specifies designs that are the reverse or inverse configuration of another entity’s designs.
-
C.
reverseDesignElement
Indicates that one design element is the reverse or inverse counterpart of another design element.
-
D.
reverseDesignSubject
Indicates that the subject is the entity for which a design or plan is derived by reversing or backtracking from an existing outcome or artifact.
-
E.
previousReverseDesign
Indicates that one entity is the immediately preceding version or design in a reverse-ordered design sequence relative to 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_69f349b6df1c81908e5e5b6c2ab6409b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f717836f0c8190b4a397bbac37dd09 |
completed | May 3, 2026, 9:38 a.m. |
| PD | Predicate disambiguation | batch_69f7127a2ff08190b77d00963c9df621 |
completed | May 3, 2026, 9:16 a.m. |
| PDg | Predicate description generation | batch_69f71782422c81908196d5e4a610cb1a |
completed | May 3, 2026, 9:38 a.m. |
Created at: May 1, 2026, 1:57 a.m.