Triple
T15816333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maddux Air Lines |
E383486
|
entity |
| Predicate | timeframeCategory |
P52643
|
FINISHED |
| Object | 1920s airline |
—
|
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: 1920s airline | Statement: [Maddux Air Lines, timeframeCategory, 1920s airline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeframeCategory Context triple: [Maddux Air Lines, timeframeCategory, 1920s airline]
-
A.
timePeriodCategory
chosen
Indicates the classification of a time period into a specific category or type (e.g., era, phase, or temporal grouping).
-
B.
timeFrameTraditionallyLinked
Indicates that one time frame is customarily or historically associated with another, reflecting a traditional linkage between them.
-
C.
timeframeRelative
Indicates a temporal relationship where one event, state, or condition is positioned in time relative to another (e.g., before, after, or overlapping).
-
D.
timeScaleCategory
Indicates the classification of an event or process based on the temporal scale or duration over which it occurs.
-
E.
timeFrameSpecified
Indicates that a specific temporal period or duration has been explicitly defined or constrained for the related event, action, or relationship.
- 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_69d86da2858c819090cc8481e7207b6e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0c4a306a48190840adc49df2c26c5 |
completed | April 16, 2026, 11:14 a.m. |
| PD | Predicate disambiguation | batch_69e0053b847c8190945726c3ddac21cc |
completed | April 15, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:49 a.m.