Triple
T35924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baltimore/Washington International Thurgood Marshall Airport |
E711
|
entity |
| Predicate | numberOfTerminals |
P2957
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Baltimore/Washington International Thurgood Marshall Airport, numberOfTerminals, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTerminals Context triple: [Baltimore/Washington International Thurgood Marshall Airport, numberOfTerminals, 1]
-
A.
numberOfStations
Indicates the total count of stations associated with or contained by a given entity.
-
B.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
-
C.
numberOfElevators
Indicates the total count of elevators associated with a given entity or location.
-
D.
branchCount
Indicates the number of branches associated with a given entity or structure.
-
E.
numberOfChambers
Indicates the count of distinct chambers or compartments associated with an entity.
- 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.