Triple
T37099010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maimonides Medical Center |
E918643
|
entity |
| Predicate | hasStrokeCenter |
P202300
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Maimonides Medical Center, hasStrokeCenter, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStrokeCenter Context triple: [Maimonides Medical Center, hasStrokeCenter, yes]
-
A.
hasStrokeContrast
Indicates that the thickness of a character’s strokes varies, distinguishing it from designs with uniform stroke weight.
-
B.
hasStrokeType
Indicates that an entity is associated with a specific type or classification of stroke.
-
C.
hasStrokeCountApprox
Indicates an approximate number of strokes associated with writing or drawing the related entity.
-
D.
hasCenterColor
Indicates that an entity possesses a central region whose color is specified or characterized.
-
E.
hasStrokeCount
Indicates the number of strokes required to write a given symbol or character.
- 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_69f76e9a48bc8190a3947508d8bca408 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a006fe981488190b4287289a3327664 |
completed | May 10, 2026, 11:45 a.m. |
| PD | Predicate disambiguation | batch_6a006f6976ec8190ba2c04fbaa946345 |
completed | May 10, 2026, 11:43 a.m. |
| PDg | Predicate description generation | batch_6a006fe8d7ac81908642e4b7c81532ac |
completed | May 10, 2026, 11:45 a.m. |
Created at: May 3, 2026, 4:14 p.m.