Triple
T26369312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gulou District, Nanjing |
E660726
|
entity |
| Predicate | hasNotableHospital |
P50261
|
FINISHED |
| Object | Nanjing Drum Tower Hospital |
—
|
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: Nanjing Drum Tower Hospital | Statement: [Gulou District, Nanjing, hasNotableHospital, Nanjing Drum Tower Hospital]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableHospital Context triple: [Gulou District, Nanjing, hasNotableHospital, Nanjing Drum Tower Hospital]
-
A.
hasNotableFacility
Indicates that an entity possesses or hosts a facility that is of particular significance, prominence, or interest.
-
B.
containsHospital
chosen
Indicates that one entity includes or encompasses a hospital within its boundaries or composition.
-
C.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
-
D.
hasAffiliatedHospital
Indicates that one entity (typically a medical professional, clinic, or organization) is formally connected or associated with a particular hospital for professional or operational purposes.
-
E.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
- 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_69ee812a698881908d6a58265995fa39 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69fd592e48cc81909d754cc6c4bd99ae |
completed | May 8, 2026, 3:31 a.m. |
| PD | Predicate disambiguation | batch_69fd58b7f9b881909dc099b28d567784 |
completed | May 8, 2026, 3:30 a.m. |
Created at: April 26, 2026, 10:57 p.m.