Triple
T72620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cupressaceae |
E1453
|
entity |
| Predicate | reproductiveType |
P4610
|
FINISHED |
| Object | monoecious in many species |
—
|
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: monoecious in many species | Statement: [Cupressaceae, reproductiveType, monoecious in many species]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reproductiveType Context triple: [Cupressaceae, reproductiveType, monoecious in many species]
-
A.
reproductiveStructure
Indicates that one entity serves as a reproductive organ or structure of another, involved in producing or facilitating the formation of offspring or reproductive cells.
-
B.
sexualDimorphism
Indicates differences in physical characteristics between males and females of a species that are systematically associated with their sex.
-
C.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
-
D.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
E.
nestType
Indicates the type or kind of nest associated with or used by an 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a252201fa481908e30791954119c17 |
completed | Feb. 28, 2026, 2:25 a.m. |
| PD | Predicate disambiguation | batch_69a24eacfdc481909e9ff99752fd42bf |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a2521ed5088190bbdfd22164bb4d94 |
completed | Feb. 28, 2026, 2:25 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.