Triple
T35923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baltimore/Washington International Thurgood Marshall Airport |
E711
|
entity |
| Predicate | numberOfRunways |
P2956
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Baltimore/Washington International Thurgood Marshall Airport, numberOfRunways, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRunways Context triple: [Baltimore/Washington International Thurgood Marshall Airport, numberOfRunways, 4]
-
A.
runwaySurface
Indicates the type or condition of the surface material that a runway is made of or covered with.
-
B.
runway
Indicates a relationship where a runway serves as the takeoff and landing surface used by aircraft at an airport or airfield.
-
C.
numberOfStations
Indicates the total count of stations associated with or contained by a given entity.
-
D.
hasInternationalAirport
Indicates that a place possesses an airport that handles international flights and services cross-border air traffic.
-
E.
hasAirportClassification
Indicates that an airport is assigned a specific classification or category based on defined criteria.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24bb753f081909cd8b25cfb8e08af |
completed | Feb. 28, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69a24ab4a6908190b6f355415ffe7948 |
completed | Feb. 28, 2026, 1:53 a.m. |
| PDg | Predicate description generation | batch_69a24bb6881081909e7d650f2b3169d3 |
completed | Feb. 28, 2026, 1:58 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.