Triple
T36409379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Samuel Loomis |
E896834
|
entity |
| Predicate | treatsPatient |
P78752
|
FINISHED |
| Object | Michael Myers |
—
|
NE NERFINISHED |
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: Michael Myers | Statement: [Dr. Samuel Loomis, treatsPatient, Michael Myers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatsPatient Context triple: [Dr. Samuel Loomis, treatsPatient, Michael Myers]
-
A.
treatsPatientsFrom
Indicates that a healthcare provider gives medical treatment or services to patients originating from a particular location or group.
-
B.
hasPatient
Indicates that an action, event, or process involves a specific entity as the one undergoing or receiving its effects (the patient).
-
C.
hasReceivedTreatmentFor
Indicates that an entity has undergone or been given a treatment in relation to a specified condition, issue, or problem.
-
D.
usesTreatment
chosen
Indicates that one entity applies or employs a particular treatment or therapeutic method on or for another entity.
-
E.
knownForTreatmentOf
Indicates that an entity is recognized or notable for providing treatment or medical care for a particular condition, disease, or type of patient.
- 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_69f76e54ce408190849acc3f7758937c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bcccd7988190aa5c931ff347d33c |
completed | May 3, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:10 p.m.