Triple
T37724499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | tuberous sclerosis complex |
E939680
|
entity |
| Predicate | hasCutaneousFeature |
P190501
|
FINISHED |
| Object | hypomelanotic macules |
—
|
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: hypomelanotic macules | Statement: [tuberous sclerosis complex, hasCutaneousFeature, hypomelanotic macules]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCutaneousFeature Context triple: [tuberous sclerosis complex, hasCutaneousFeature, hypomelanotic macules]
-
A.
skinCharacteristic
Indicates a relationship where an entity is associated with a particular quality, feature, or condition of its skin.
-
B.
hasClinicalFeature
chosen
Indicates that an entity (such as a disease, condition, or case) exhibits or is characterized by a particular clinical sign, symptom, or feature.
-
C.
hasPhysicalFeature
Indicates that one entity possesses or exhibits a specific physical characteristic or feature of another entity.
-
D.
hasFacialSkinColor
Indicates that one entity has a specific facial skin color characterized or attributed by another entity.
-
E.
skinVariant
Indicates a relationship where one entity is an alternative skin, appearance, or visual variant of another 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_69f76edc208c8190bc8b9683f75e1024 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fe86cad5108190b0164b8bc6fc23ea |
completed | May 9, 2026, 12:58 a.m. |
| PD | Predicate disambiguation | batch_69fe83c0c9888190b6fc40c7f727b569 |
completed | May 9, 2026, 12:45 a.m. |
Created at: May 3, 2026, 4:18 p.m.