Triple
T35412728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | De Materia Medica |
E1023554
|
entity |
| Predicate | describesApproximateNumberOfPlants |
P57653
|
FINISHED |
| Object | about 500 |
—
|
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: about 500 | Statement: [De Materia Medica, describesApproximateNumberOfPlants, about 500]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: describesApproximateNumberOfPlants Context triple: [De Materia Medica, describesApproximateNumberOfPlants, about 500]
-
A.
numberOfPlants
chosen
Indicates the total count of plants associated with a given entity or context.
-
B.
plantHeight
Indicates the measured vertical size or growth extent of a plant from its base to its top.
-
C.
approximateNumberOfTulips
Indicates that the relationship specifies an estimated or approximate count of tulips associated with an entity.
-
D.
hasAttractiveFoliage
Indicates that an entity possesses foliage that is visually appealing or ornamental in appearance.
-
E.
minimumPlantingDensity
Indicates the smallest allowable number of plants per unit area at which planting should occur.
- 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_69f76df54bac8190bd0d3b0eb35cda5f |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fd44474ed48190ac372e4c88d762ed |
completed | May 8, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69fd41ef28a48190a66959be5c964461 |
completed | May 8, 2026, 1:52 a.m. |
Created at: May 3, 2026, 4:03 p.m.