Triple
T2708712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jacksonville International Airport |
E59805
|
entity |
| Predicate | numberOfConcourses |
P18237
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Jacksonville International Airport, numberOfConcourses, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfConcourses Context triple: [Jacksonville International Airport, numberOfConcourses, 2]
-
A.
hasNumberOfConcourses
chosen
Indicates the relationship specifying how many concourses are associated with a given entity.
-
B.
numberOfCases
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
-
C.
numberOfConflicts
Indicates the count of distinct conflicts associated with or involving a given entity or situation.
-
D.
numberOfCourts
Indicates the quantity of courts associated with or present at a given entity or location.
-
E.
defendantCount
Indicates the number of defendants involved in a particular legal case or proceeding.
- F. None of above.
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_69ab4ac92a088190bc74bca14038e3de |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda7542548190bbf6c947145f7f63 |
completed | March 7, 2026, 7:57 a.m. |
| PD | Predicate disambiguation | batch_69abd8224c688190bb4a362360b03007 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:55 p.m.