Triple
T4328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hospital of the University of Pennsylvania |
E83
|
entity |
| Predicate | hasHelipad |
P105
|
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: [Hospital of the University of Pennsylvania, hasHelipad, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHelipad Context triple: [Hospital of the University of Pennsylvania, hasHelipad, yes]
-
A.
hasMajorAirport
Indicates that a location possesses at least one significant airport that serves as a primary hub for air travel in that area.
-
B.
hasNotableFacility
chosen
Indicates that an entity possesses or hosts a facility that is of particular significance, prominence, or interest.
-
C.
largestAirport
Indicates that one airport is the largest (typically by area, traffic, or capacity) among a specified set or within a given region.
-
D.
headquartersLocation
Indicates the place where an organization’s main administrative center or principal office is located.
-
E.
hasMajorCity
Indicates that a location possesses at least one city of significant size, importance, or influence within its region or country.
- 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_69a238d6b47881909e68288aed2fd858 |
completed | Feb. 28, 2026, 12:37 a.m. |
| NER | Named-entity recognition | batch_69a23c24b3d08190a714126292fd5479 |
completed | Feb. 28, 2026, 12:51 a.m. |
| PD | Predicate disambiguation | batch_69a23998af288190855f0456740cbd51 |
completed | Feb. 28, 2026, 12:40 a.m. |
Created at: Feb. 28, 2026, 12:40 a.m.