Triple
T19184539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | College of Medicine |
E469666
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Mobile |
—
|
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: Mobile | Statement: [College of Medicine, city, Mobile]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mobile Context triple: [College of Medicine, city, Mobile]
-
A.
Mobile
Mobile is a small coastal community in Newfoundland and Labrador, Canada, located on the Avalon Peninsula south of St. John’s.
-
B.
Mobile
chosen
Mobile is a historic port city on Alabama’s Gulf Coast known for its shipbuilding, cultural heritage, and hosting one of the oldest Mardi Gras celebrations in the United States.
-
C.
Handphone
Handphone is a South Korean thriller film known for its tense narrative involving blackmail and the misuse of mobile phone technology.
-
D.
.mobi
.mobi is a top-level internet domain designed specifically for websites and services optimized for mobile devices.
-
E.
Mobile Unit
Mobile Unit is a touring theater program of The Public Theater that brings free, professional performances to underserved communities and nontraditional venues.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f61f1d9c8190b67555383d821958 |
completed | April 20, 2026, 9:47 a.m. |
Created at: April 10, 2026, 12:07 p.m.