Triple
T5977415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ENBA |
E133031
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object | ENBA |
E133031
|
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: ENBA | Statement: [ENBA, hasAbbreviation, ENBA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ENBA Context triple: [ENBA, hasAbbreviation, ENBA]
-
A.
ENBA
chosen
ENBA is the commonly used abbreviation for the Escola Nacional de Belas Artes, a prominent national fine arts school.
-
B.
ENSBA
ENSBA is the commonly used abbreviation for the École des Beaux-Arts, a prestigious French fine arts school renowned for its influence on art and architecture.
-
C.
EBA
EBA is the European Union’s regulatory agency responsible for overseeing and harmonizing banking supervision and ensuring financial stability across member states.
-
D.
EBA
EBA is the IATA airport code for Marina di Campo Airport, which serves Italy’s Elba Island in the Tyrrhenian Sea.
-
E.
ENA
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
- 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_69c0086f45e8819098f73dd16d45ec9d |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04a3cffb08190a764a404a4ce5812 |
completed | March 22, 2026, 7:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0e4184a708190a9e4fe8453463a4b |
completed | March 23, 2026, 6:56 a.m. |
Created at: March 22, 2026, 4:04 p.m.