Triple
T5392169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Free Spirit |
E120356
|
entity |
| Predicate | pointsEarnedFrom |
P62973
|
FINISHED |
| Object | base fare |
—
|
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: base fare | Statement: [Free Spirit, pointsEarnedFrom, base fare]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsEarnedFrom Context triple: [Free Spirit, pointsEarnedFrom, base fare]
-
A.
winnerPoints
Indicates the number of points earned by the winning participant or entity in a competition or event.
-
B.
careerPoints
Indicates the total number of points an individual has accumulated over the course of their entire career in a given activity or domain.
-
C.
earnsMorePointsThan
Indicates that one entity receives a greater number of points than another entity in a given context or comparison.
-
D.
pointsForWin
Indicates the number of points awarded to an entity for achieving a win in a given context or competition.
-
E.
pointsScored
Indicates the number of points an entity has earned or achieved in a particular event, game, or context.
- 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_69bd46354c648190a38b26f107010a96 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd8719ff04819089e3a90f90b5e3fc |
completed | March 20, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_69bd8463a9c88190bd760378f3026180 |
completed | March 20, 2026, 5:31 p.m. |
| PDg | Predicate description generation | batch_69bd853005088190b1b092a9beb090b2 |
completed | March 20, 2026, 5:34 p.m. |
Created at: March 20, 2026, 2:04 p.m.