Triple
T2697063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gassed |
E58535
|
entity |
| Predicate | depictsMedicalCondition |
P41129
|
FINISHED |
| Object | chemical eye injury |
—
|
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: chemical eye injury | Statement: [Gassed, depictsMedicalCondition, chemical eye injury]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsMedicalCondition Context triple: [Gassed, depictsMedicalCondition, chemical eye injury]
-
A.
depicts
Indicates that one entity visually represents, portrays, or shows another entity.
-
B.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
C.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
-
D.
depictsName
Indicates that something visually represents or portrays the name of an entity.
-
E.
featuresDisease
Indicates that an entity exhibits, presents, or is characterized by a particular disease.
- 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_69ab4ac269e481909cb317d79e68b75b |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda3112108190a5e49c13368cf83f |
completed | March 7, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69abd81ea5d88190ab5c8f8b8064b931 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd879bb808190bd2c34de1664c816 |
completed | March 7, 2026, 7:49 a.m. |
Created at: March 6, 2026, 9:55 p.m.