Triple
T3836195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loyola University Chicago School of Law |
E91137
|
entity |
| Predicate | offersDegree |
P49
|
FINISHED |
| Object |
MJ
MJ is a Master of Jurisprudence graduate law degree designed for non-lawyers seeking advanced legal knowledge in a specific field.
|
E392310
|
NE FINISHED |
How this triple was built (4 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: MJ | Statement: [Loyola University Chicago School of Law, offersDegree, MJ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MJ Context triple: [Loyola University Chicago School of Law, offersDegree, MJ]
-
A.
MJ
MJ is the widely used nickname for Michael Jordan, the legendary American basketball player often regarded as the greatest in NBA history.
-
B.
MJ
MJ is a reimagined version of the Mary Jane Watson character who appears as Peter Parker’s sharp, observant classmate and love interest in the Marvel Cinematic Universe Spider-Man films.
-
C.
JM
JM is the two-letter ISO 3166-1 alpha-2 country code assigned to Jamaica for international standardization and identification purposes.
-
D.
MZ
MZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Mozambique.
-
E.
MK
MK is the commonly used abbreviation for Umkhonto we Sizwe, the former armed wing of South Africa’s African National Congress during the anti-apartheid struggle.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: MJ Triple: [Loyola University Chicago School of Law, offersDegree, MJ]
Generated description
MJ is a Master of Jurisprudence graduate law degree designed for non-lawyers seeking advanced legal knowledge in a specific field.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MJ Target entity description: MJ is a Master of Jurisprudence graduate law degree designed for non-lawyers seeking advanced legal knowledge in a specific field.
-
A.
MJ
MJ is the widely used nickname for Michael Jordan, the legendary American basketball player often regarded as the greatest in NBA history.
-
B.
MJ
MJ is a reimagined version of the Mary Jane Watson character who appears as Peter Parker’s sharp, observant classmate and love interest in the Marvel Cinematic Universe Spider-Man films.
-
C.
JM
JM is the two-letter ISO 3166-1 alpha-2 country code assigned to Jamaica for international standardization and identification purposes.
-
D.
MZ
MZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Mozambique.
-
E.
MK
MK is the commonly used abbreviation for Umkhonto we Sizwe, the former armed wing of South Africa’s African National Congress during the anti-apartheid struggle.
- F. None of above. chosen
Provenance (5 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_69aed960b538819096561c8ed448dec9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeeb9baa508190800e73bf186f046e |
completed | March 9, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b50405264c8190b145dc3929ffc940 |
completed | March 14, 2026, 6:45 a.m. |
| NEDg | Description generation | batch_69b504c46dcc8190a9775c39e5c734a9 |
completed | March 14, 2026, 6:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b505742830819093a861bde17c03c0 |
completed | March 14, 2026, 6:51 a.m. |
Created at: March 9, 2026, 3:18 p.m.