Triple
T23555841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruta |
E578183
|
entity |
| Predicate | photosensitivityRisk |
P127606
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Ruta, photosensitivityRisk, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: photosensitivityRisk Context triple: [Ruta, photosensitivityRisk, true]
-
A.
photosensitivity
Indicates a relationship where an entity reacts or responds in a particular way when exposed to light.
-
B.
sensitivityToLight
chosen
Indicates a relationship where an entity reacts adversely or more strongly than normal when exposed to light.
-
C.
effectOnSkin
Indicates the impact or influence that something has on the condition, appearance, or health of skin.
-
D.
skinContactSuitability
Indicates that one entity is suitable, safe, or appropriate for direct contact with the skin of another entity.
-
E.
hasRiskFrom
Indicates that one entity is exposed to or may suffer potential harm, loss, or adverse effects as a result 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_69e245fa93448190919cb04534560542 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1aed37f248190992f040f4d22f0bf |
completed | April 29, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69f118afabd88190bd88f49597d120e8 |
completed | April 28, 2026, 8:29 p.m. |
Created at: April 17, 2026, 6:12 p.m.