Triple
T3761888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Albuquerque International Balloon Fiesta |
E82180
|
entity |
| Predicate | typicalNumberOfBalloons |
P50799
|
FINISHED |
| Object | hundreds |
—
|
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: hundreds | Statement: [Albuquerque International Balloon Fiesta, typicalNumberOfBalloons, hundreds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfBalloons Context triple: [Albuquerque International Balloon Fiesta, typicalNumberOfBalloons, hundreds]
-
A.
numberOfAttractions
Indicates the total count of attractions associated with a given entity or context.
-
B.
numberOfGondolas
Indicates the quantity of gondolas associated with a given entity or system.
-
C.
numberOfLights
Indicates the quantity of lights associated with or present on a given entity.
-
D.
numberOfTubes
Indicates the quantity of tubes associated with or contained by a given entity.
-
E.
pillarNumber
Indicates the specific numerical identifier assigned to a particular pillar within a set or structure.
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbfa44ac819082f2c895d96c9170 |
completed | March 8, 2026, 7:20 p.m. |
| PD | Predicate disambiguation | batch_69adc04c851c8190ae5eaebf36df539b |
completed | March 8, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69adc0fe3e3c8190bd886c7745c172a0 |
completed | March 8, 2026, 6:33 p.m. |
Created at: March 8, 2026, 3:35 p.m.