Triple
T17803538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep Q-Learning |
E444494
|
entity |
| Predicate | canSufferFrom |
P583
|
FINISHED |
| Object | overestimation bias |
—
|
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: overestimation bias | Statement: [Deep Q-Learning, canSufferFrom, overestimation bias]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canSufferFrom Context triple: [Deep Q-Learning, canSufferFrom, overestimation bias]
-
A.
sufferedCondition
Indicates that an entity experienced or was afflicted by a particular condition, typically adverse or harmful, at some point in time.
-
B.
hasPossibleSymptom
Indicates that an entity (such as a condition or disease) may be associated with a particular symptom that can potentially occur.
-
C.
mayBeComorbidWith
Indicates that two conditions or disorders can occur together in the same individual, potentially influencing each other’s presence or severity.
-
D.
susceptibleTo
chosen
Indicates that one entity is vulnerable or likely to be affected, harmed, or influenced by another entity or factor.
-
E.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
- 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_69d8b9efe370819095cd219b143ae727 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4880171608190be2088c7a387bfb7 |
completed | April 19, 2026, 7:45 a.m. |
| PD | Predicate disambiguation | batch_69e3d8de28688190844b65acf6af54e6 |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:13 a.m.