Triple
T18386221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grady Memorial Hospital |
E446598
|
entity |
| Predicate | hasTraumaLevel |
P25803
|
FINISHED |
| Object | Level I |
—
|
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: Level I | Statement: [Grady Memorial Hospital, hasTraumaLevel, Level I]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraumaLevel Context triple: [Grady Memorial Hospital, hasTraumaLevel, Level I]
-
A.
traumaLevel
chosen
Indicates the degree or severity of trauma experienced or present in relation to an entity or event.
-
B.
hasCasualtiesLevel
Indicates the severity or extent of casualties associated with an event, incident, or situation.
-
C.
hasTypeOfDamage
Indicates that an entity experiences or exhibits a specific kind or category of damage.
-
D.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
E.
hasApproximateNumberOfWounds
Indicates that an entity has a number of wounds that is known only approximately rather than as an exact count.
- 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_69d8b9f370b88190b1e5081c2c238e7f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e517a00c608190a8f0010c7b53df90 |
completed | April 19, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69e44ff1f92c8190afbb8e85d12bf2a9 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:46 a.m.