Triple
T4564783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Multivariate ENSO Index |
E121882
|
entity |
| Predicate | acronymOf |
P8733
|
FINISHED |
| Object | MEI |
E452744
|
NE 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: MEI | Statement: [Multivariate ENSO Index, acronymOf, MEI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MEI Context triple: [Multivariate ENSO Index, acronymOf, MEI]
-
A.
MEI
MEI is the vehicle registration code for the German town of Meissen in the state of Saxony.
-
B.
MEI
chosen
MEI is a climate index that quantifies the strength and phase of the El Niño–Southern Oscillation by combining multiple atmospheric and oceanic variables over the tropical Pacific.
-
C.
MEEI
MEEI is a renowned specialty hospital in Boston focused on ophthalmology and otolaryngology, affiliated with Harvard Medical School.
-
D.
MITEI
MITEI is the Massachusetts Institute of Technology’s multidisciplinary research and education hub focused on advancing energy technologies, policy, and innovation for a low-carbon future.
-
E.
MEC
MEC is the commonly used acronym for Uruguay’s Ministry of Education and Culture, the national body responsible for educational policy and cultural affairs.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd463f156881908a99aca69c5721ac |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd589b439c81908da9d19433310bcd |
completed | March 20, 2026, 2:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bde0766e70819080159402ca147bf5 |
completed | March 21, 2026, 12:04 a.m. |
Created at: March 20, 2026, 1:09 p.m.