Triple
T10891857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Satsuma mandarins |
E257195
|
entity |
| Predicate | skinAdherence |
P8035
|
FINISHED |
| Object | very loose to segments |
—
|
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: very loose to segments | Statement: [Satsuma mandarins, skinAdherence, very loose to segments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: skinAdherence Context triple: [Satsuma mandarins, skinAdherence, very loose to segments]
-
A.
skinCharacteristic
chosen
Indicates a relationship where an entity is associated with a particular quality, feature, or condition of its skin.
-
B.
skinThickness
Indicates the measured thickness of an entity’s skin, typically quantifying how thick its outer tissue layer is.
-
C.
skeletonCoating
Indicates that one entity serves as a coating, covering, or outer layer on the skeleton of another entity.
-
D.
cuticleFunction
Indicates the functional role or purpose that a cuticle serves in relation to an organism or structure.
-
E.
coatingType
Indicates the type or kind of coating applied to or associated with an entity.
- 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75206354881908b148f2df3938513 |
completed | April 9, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69d70d3943c881908895397eccc3e415 |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:21 p.m.