Triple
T6775911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | École nationale d'administration |
E155153
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | ENA |
E155153
|
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: ENA | Statement: [École nationale d'administration, shortName, ENA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ENA Context triple: [École nationale d'administration, shortName, ENA]
-
A.
ENA
chosen
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
-
B.
ENE
ENE was the stock ticker symbol for Enron Corporation, the American energy company infamous for its massive accounting fraud and subsequent 2001 bankruptcy.
-
C.
ANA
ANA is the commonly used abbreviation for the Afghan National Army, the former main land warfare branch of Afghanistan’s armed forces.
-
D.
ANA
ANA is the Portuguese company responsible for managing and operating the main airports in Portugal.
-
E.
ANA
ANA is the ICAO airline designator for All Nippon Airways, Japan’s largest airline and a major global carrier.
- 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_69c68812ef7c819099369f51febb725c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d24f77c88190be21cf4ef132aa31 |
completed | March 27, 2026, 6:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c712cc9ff08190bb7ec0bf4cc4db01 |
completed | March 27, 2026, 11:29 p.m. |
Created at: March 27, 2026, 2:13 p.m.