Triple
T107662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orly Airport |
E2174
|
entity |
| Predicate | servesAirlineAsHub |
P4364
|
FINISHED |
| Object |
French Bee
French Bee is a French low-cost, long-haul airline specializing in transatlantic and Indian Ocean routes.
|
E10909
|
NE FINISHED |
How this triple was built (4 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: French Bee | Statement: [Orly Airport, servesAirlineAsHub, French Bee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: French Bee Context triple: [Orly Airport, servesAirlineAsHub, French Bee]
-
A.
Mouton
Mouton is an academic publishing house known for its influential works in linguistics and related fields.
-
B.
Collex-Bossy
Collex-Bossy is a small municipality in the canton of Geneva in southwestern Switzerland, known for its rural character and proximity to the French border.
-
C.
Gallic rooster
The Gallic rooster is a traditional emblem of France, symbolizing the nation’s pride, vigilance, and cultural identity.
-
D.
Burgundy
Burgundy is a renowned wine-producing region in eastern France, famous for its high-quality Chardonnay and Pinot Noir wines.
-
E.
The Ants
The Ants is a Pulitzer Prize–winning scientific book that provides a comprehensive and authoritative overview of ant biology, behavior, and social organization.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: French Bee Triple: [Orly Airport, servesAirlineAsHub, French Bee]
Generated description
French Bee is a French low-cost, long-haul airline specializing in transatlantic and Indian Ocean routes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: French Bee Target entity description: French Bee is a French low-cost, long-haul airline specializing in transatlantic and Indian Ocean routes.
-
A.
Mouton
Mouton is an academic publishing house known for its influential works in linguistics and related fields.
-
B.
Collex-Bossy
Collex-Bossy is a small municipality in the canton of Geneva in southwestern Switzerland, known for its rural character and proximity to the French border.
-
C.
Gallic rooster
The Gallic rooster is a traditional emblem of France, symbolizing the nation’s pride, vigilance, and cultural identity.
-
D.
Burgundy
Burgundy is a renowned wine-producing region in eastern France, famous for its high-quality Chardonnay and Pinot Noir wines.
-
E.
The Ants
The Ants is a Pulitzer Prize–winning scientific book that provides a comprehensive and authoritative overview of ant biology, behavior, and social organization.
- F. None of above. chosen
Provenance (5 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a25b7e2c188190b1dd8aafd4507a99 |
completed | Feb. 28, 2026, 3:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a275e81aa48190827b634822e25058 |
completed | Feb. 28, 2026, 4:58 a.m. |
| NEDg | Description generation | batch_69a2772d526881909052faae2b3b9829 |
completed | Feb. 28, 2026, 5:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a277a7c6a08190820993e8de80d21a |
completed | Feb. 28, 2026, 5:05 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.