Triple
T15997186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 17R/35L |
E388000
|
entity |
| Predicate | hasUsageIntensity |
P85306
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Runway 17R/35L, hasUsageIntensity, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUsageIntensity Context triple: [Runway 17R/35L, hasUsageIntensity, high]
-
A.
hasUsageLevel
chosen
Indicates the degree or intensity with which something is used or utilized.
-
B.
usagePeak
Indicates that the usage or consumption of something reaches its highest level or intensity during a particular time or condition.
-
C.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
-
D.
hasHistoricalUsageIn
Indicates that something has been used or practiced within a particular historical period, context, or tradition.
-
E.
maximumUsage
Indicates the highest allowable or observed amount, frequency, or extent to which something can be used within a defined context or period.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4e871c819082d7b1c1eaf5b4fe |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d9d8e881909b559a3e3ca21d24 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:55 a.m.