Triple
T169065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | G-Men |
E3077
|
entity |
| Predicate | refersToFranchise |
P37
|
FINISHED |
| Object | New York Giants 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: New York Giants franchise history | Statement: [G-Men, refersToFranchise, New York Giants franchise history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: refersToFranchise Context triple: [G-Men, refersToFranchise, New York Giants franchise history]
-
A.
affectedFranchise
Indicates that one entity has an impact on, or brings about a change in the status or condition of, a franchise.
-
B.
refersToRole
Indicates that one entity designates, mentions, or points to another entity specifically in its capacity as a role or position.
-
C.
appearsIn
Indicates that an entity is present, featured, or occurs within a particular context, work, or medium.
-
D.
canRefer
Indicates that one entity has the ability or permission to mention, point to, or direct attention to another entity.
-
E.
relatedTo
chosen
Indicates a general, non-specific relationship or association exists between two entities.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a258b6f4f88190b1264bbbeb19a29e |
completed | Feb. 28, 2026, 2:53 a.m. |
| PD | Predicate disambiguation | batch_69a25665f5b8819096ca3e084faf976e |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.