Triple
T7226050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rod of Asclepius |
E154780
|
entity |
| Predicate | differenceFromCaduceus |
P75719
|
FINISHED |
| Object | has one serpent and no wings |
—
|
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: has one serpent and no wings | Statement: [Rod of Asclepius, differenceFromCaduceus, has one serpent and no wings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: differenceFromCaduceus Context triple: [Rod of Asclepius, differenceFromCaduceus, has one serpent and no wings]
-
A.
differenceDescription
Indicates a textual explanation that characterizes how two entities differ from each other.
-
B.
differIn
Indicates that two entities are not the same in at least one specified aspect, attribute, or value.
-
C.
differenceFromTCP
Indicates a relationship where one protocol, configuration, or behavior is characterized specifically by how it differs from TCP.
-
D.
differenceFromStates
Indicates that one state or condition is distinct from, or deviates in some way from, another state or condition.
-
E.
differenceFromSidecar
Indicates that one entity differs in some specified way from a corresponding or reference entity referred to as a "sidecar."
- 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_69c68811dd1c8190ac460bb39e64e1f0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6e9de21e081908f30700f6211c5ef |
completed | March 27, 2026, 8:34 p.m. |
| PD | Predicate disambiguation | batch_69c6e761b7fc8190857794d78af1b468 |
completed | March 27, 2026, 8:24 p.m. |
| PDg | Predicate description generation | batch_69c6e8b5f6508190af28e06a7959d717 |
completed | March 27, 2026, 8:29 p.m. |
Created at: March 27, 2026, 2:54 p.m.