Triple
T5905592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Hibiscus |
E131333
|
entity |
| Predicate | etymologyTheme |
P66850
|
FINISHED |
| Object | flower imagery |
—
|
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: flower imagery | Statement: [City of Hibiscus, etymologyTheme, flower imagery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: etymologyTheme Context triple: [City of Hibiscus, etymologyTheme, flower imagery]
-
A.
etymology
Indicates the historical origin and development of a word or term, including its source language and form.
-
B.
etymologyType
Indicates the specific kind or category of etymological relationship that links a term to its linguistic origin or source.
-
C.
etymologicalField
Indicates that one term belongs to a particular semantic or conceptual domain relevant to its etymological origin or historical development.
-
D.
etymologyReason
Indicates the reason, source, or origin explaining how or why a term acquired its particular etymology.
-
E.
etymologyGloss
Indicates that a term’s meaning is explained by a brief gloss specifically describing its etymological origin or source.
- 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_69c0085864a88190a569c05ff7d65f29 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03ee10b308190afe38b904ae7c5f7 |
completed | March 22, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69c0334fcf6481908e8e74105de9d49b |
completed | March 22, 2026, 6:22 p.m. |
| PDg | Predicate description generation | batch_69c03edf98b881908e9dbc03d3fd6218 |
completed | March 22, 2026, 7:11 p.m. |
Created at: March 22, 2026, 3:59 p.m.