Triple
T5475802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York Riptide |
E122948
|
entity |
| Predicate | hasFranchiseType |
P31018
|
FINISHED |
| Object | expansion team |
—
|
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: expansion team | Statement: [New York Riptide, hasFranchiseType, expansion team]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFranchiseType Context triple: [New York Riptide, hasFranchiseType, expansion team]
-
A.
hasFranchiseSlot
Indicates that an entity holds or is assigned a specific franchise position, license, or allocation within a larger franchising structure.
-
B.
hasFranchiseModel
Indicates that one entity operates under, offers, or is associated with a business franchise system or structure defined by another entity.
-
C.
controlsFranchiseType
Indicates that one entity has authority to determine or manage the type or category of franchise associated with another entity.
-
D.
typeOfFranchise
chosen
Indicates the specific category or kind of franchise that an entity belongs to within a broader franchising system.
-
E.
hadFranchise
Indicates that one entity possessed or operated a franchise right or franchise unit of another entity.
- 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_69bd46459ff48190823377457bcf7128 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd923465c88190ad9c1b75b268f7ca |
completed | March 20, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69bd91a58c448190904964a439045e05 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:09 p.m.