Triple
T5691070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rose of Sharon |
E125427
|
entity |
| Predicate | otherCommonName |
P65979
|
FINISHED |
| Object | shrubby hibiscus |
—
|
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: shrubby hibiscus | Statement: [Rose of Sharon, otherCommonName, shrubby hibiscus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: otherCommonName Context triple: [Rose of Sharon, otherCommonName, shrubby hibiscus]
-
A.
commonName
Indicates that one entity is the commonly used or vernacular name by which the other entity is known.
-
B.
commonNameOf
Indicates that one entity is the commonly used or popular name by which the other entity is known.
-
C.
notableMemberCommonName
Indicates that the object is the commonly used or well-known name of a notable member associated with the subject.
-
D.
includesCommonName
Indicates that one entity contains or specifies a commonly used (non-scientific) name for another entity.
-
E.
referredAircraftCommonName
Indicates that one entity mentions or identifies an aircraft using its commonly known name.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c0e0408190ab6c3cd3f907e80f |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c028fec2bc819083f5dca6a8d9d435 |
completed | March 22, 2026, 5:38 p.m. |
Created at: March 22, 2026, 3:44 p.m.