Triple
T4201686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orly Sud |
E86080
|
entity |
| Predicate | formerNameOf |
P65
|
FINISHED |
| Object | Orly 4 |
E13071
|
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: Orly 4 | Statement: [Orly Sud, formerNameOf, Orly 4]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orly 4 Context triple: [Orly Sud, formerNameOf, Orly 4]
-
A.
Orly 4
chosen
Orly 4 is one of the main passenger terminals at Paris Orly Airport, serving as a hub for various domestic and international flights.
-
B.
Orly 3
Orly 3 is one of the main passenger terminals at Paris Orly Airport, serving as a hub for check-in, boarding, and arrivals operations.
-
C.
Orly 2
Orly 2 is one of the main passenger terminals at Paris Orly Airport, serving as a hub for various domestic and international flights.
-
D.
Orly 1
Orly 1 is one of the passenger terminals at Paris Orly Airport, serving as a key facility for check-in, boarding, and arrivals operations.
-
E.
Orly
Orly is a commune in the southern suburbs of Paris, France, best known for giving its name to the nearby Paris Orly Airport.
- 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_69aed93b89f48190a31f6d57c760e42f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af037da30481908106b27a88d59140 |
completed | March 9, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b58a14eda88190aaca14644c3e041a |
completed | March 14, 2026, 4:17 p.m. |
Created at: March 9, 2026, 3:49 p.m.