Triple
T4201699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orly 4 |
E86080
|
entity |
| Predicate | successorOf |
P78
|
FINISHED |
| Object | Orly Sud |
E86080
|
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 Sud | Statement: [Orly 4, successorOf, Orly Sud]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orly Sud Context triple: [Orly 4, successorOf, Orly Sud]
-
A.
Orly Sud
chosen
Orly Sud is the former name of Orly 4, a terminal facility at Paris Orly Airport in France.
-
B.
Orna Berry
Orna Berry is an Israeli computer scientist, high-tech entrepreneur, and former Chief Scientist of Israel, recognized as a pioneering woman in the country’s technology and innovation sectors.
-
C.
Orna Grumberg
Orna Grumberg is a prominent computer scientist known for her contributions to formal verification and model checking.
-
D.
Basya Cohen
Basya Cohen, better known as Betty Comden, was an American lyricist, screenwriter, and performer famed for her influential work on classic Broadway musicals and Hollywood films.
-
E.
Ayelet Zurer
Ayelet Zurer is an Israeli actress known internationally for her roles in films such as "Angels & Demons," "Munich," and "Man of Steel."
- 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_69b5c70883c081909ea4d300f61b295e |
completed | March 14, 2026, 8:37 p.m. |
Created at: March 9, 2026, 3:49 p.m.