Triple
T31216303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Empirical analysis of the Mariel boatlift and its impact on Miami labor markets |
E795879
|
entity |
| Predicate | typeOfShock |
P200944
|
FINISHED |
| Object | immigration shock |
—
|
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: immigration shock | Statement: [Empirical analysis of the Mariel boatlift and its impact on Miami labor markets, typeOfShock, immigration shock]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfShock Context triple: [Empirical analysis of the Mariel boatlift and its impact on Miami labor markets, typeOfShock, immigration shock]
-
A.
shockInteractionObserved
Indicates that an interaction involving an electric or physical shock between entities has been detected or recorded.
-
B.
injuryType
Indicates the specific kind or category of injury associated with an entity or event.
-
C.
typeOfRescue
Indicates the specific method or category of rescue operation performed in a rescue event.
-
D.
excitationType
Indicates the specific manner or mechanism by which an entity is excited or brought to a higher energy or activity state.
-
E.
traumaSource
Indicates that one entity is the origin or cause of another entity’s trauma.
- F. None of above. chosen
Provenance (4 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_69f224d9d52c8190a61f68ded37fa755 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69ffbb1c5bf88190a0bf791213045885 |
completed | May 9, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69ffba0ab0f881908f84ef81f7a1bfe8 |
completed | May 9, 2026, 10:49 p.m. |
| PDg | Predicate description generation | batch_69ffbb1b3b888190ba329ad321fc3f3d |
completed | May 9, 2026, 10:54 p.m. |
Created at: April 29, 2026, 9:10 p.m.