Triple
T2753658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Snyder |
E61047
|
entity |
| Predicate | soldFranchiseInYear |
P929
|
FINISHED |
| Object | 2023 |
—
|
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: 2023 | Statement: [Dan Snyder, soldFranchiseInYear, 2023]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: soldFranchiseInYear Context triple: [Dan Snyder, soldFranchiseInYear, 2023]
-
A.
saleYear
chosen
Indicates the calendar year in which a sale transaction took place.
-
B.
tradeYear
Indicates the specific calendar year in which a trade or commercial transaction took place or was recorded.
-
C.
distributesFranchise
Indicates that one party grants or allocates franchise rights or opportunities to another party.
-
D.
franchiseStatus
Indicates the current standing or condition of a franchise relationship between parties, such as whether it is active, inactive, pending, or terminated.
-
E.
franchiseRecord
Indicates a record of performance, achievement, or statistics that is the best or most significant within a particular franchise or organization.
- 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_69ab4b7a85bc819094a349b84beb1f2c |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb6ed9c08190824d1866e198ef80 |
completed | March 7, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69abd82d005c81908a1ac7a1313c6d88 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:56 p.m.