Triple
T525523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Orly Airport |
E10907
|
entity |
| Predicate | openedAsCivilAirport |
P15153
|
FINISHED |
| Object | 1930s |
—
|
LITERAL 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: 1930s | Statement: [Paris Orly Airport, openedAsCivilAirport, 1930s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: openedAsCivilAirport Context triple: [Paris Orly Airport, openedAsCivilAirport, 1930s]
-
A.
openedAsMunicipalAirport
Indicates that an airport was initially established and began operation as a municipal (city- or town-operated) airport.
-
B.
openedAsICAOcode
Indicates that an airport or airfield began operations under the specified ICAO airport code.
-
C.
openedAsMilitaryAirfield
Indicates that an airfield was originally established and began operation specifically for military aviation use.
-
D.
hubAirport
Indicates that an airport serves as a primary hub or central operating base for a particular airline or carrier.
-
E.
airportRole
Indicates that an entity serves a specific functional role or capacity within the context of an airport.
- F. None of above. chosen
Provenance (4 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_69a2e84b16c4819088d284c47c3a7968 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1b7f448819087e5e7f3b37d7142 |
completed | Feb. 28, 2026, 1:46 p.m. |
| PD | Predicate disambiguation | batch_69a2f0198ecc8190883849e5a8245963 |
completed | Feb. 28, 2026, 1:39 p.m. |
| PDg | Predicate description generation | batch_69a2f0dcff1881909c18e8c599c150a1 |
completed | Feb. 28, 2026, 1:42 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.