Triple
T28580676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2009 New Orleans Saints season |
E723364
|
entity |
| Predicate | divisionTitleCountToDate |
P19826
|
FINISHED |
| Object | third division title in franchise history |
—
|
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: third division title in franchise history | Statement: [2009 New Orleans Saints season, divisionTitleCountToDate, third division title in franchise history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: divisionTitleCountToDate Context triple: [2009 New Orleans Saints season, divisionTitleCountToDate, third division title in franchise history]
-
A.
divisionTitleCount
chosen
Indicates the number of titles or championships associated with a particular division.
-
B.
divisionYear
Indicates the year in which a division or split of an entity took place.
-
C.
divisionCountRelation
Indicates a relationship where one entity’s quantity is divided by another’s, specifying how many times one value is contained within or partitions the other.
-
D.
divisionTitle
Indicates the formal name or title assigned to a specific division within a larger organization or structure.
-
E.
divisionFrequency
Indicates how often a division event occurs within a given context or time frame.
- 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_69f01d7e97708190ae9e77ee66a68abd |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f65f7731e4819099d5bd3d915ee266 |
completed | May 2, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f65c2198208190a3954086c22cfcbf |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 28, 2026, 4:14 a.m.