Triple
T10156716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grays Sports Almanac |
E233792
|
entity |
| Predicate | hasRealWorldMerchandise |
P45835
|
FINISHED |
| Object | replica book |
—
|
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: replica book | Statement: [Grays Sports Almanac, hasRealWorldMerchandise, replica book]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRealWorldMerchandise Context triple: [Grays Sports Almanac, hasRealWorldMerchandise, replica book]
-
A.
hasMerchandiseTieIn
chosen
Indicates that one entity has a commercial or promotional product or line (merchandise) that is directly tied to, branded with, or derived from another entity.
-
B.
usedInMerchandise
Indicates that something (such as a work, character, or design) is utilized or featured as part of a merchandise item.
-
C.
hasRealModel
Indicates that an abstract, theoretical, or simplified entity is associated with a corresponding concrete or physically instantiated model in the real world.
-
D.
hasFictionalCollector
Indicates that an entity is associated with a fictional character who collects or curates it.
-
E.
hasRetailProduct
Indicates that an entity offers, sells, or makes available a particular product in a retail context.
- 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_69ca848e80748190b91d1e04d35512c7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec3c47dc81909679903e6024eb49 |
completed | April 2, 2026, 4:10 a.m. |
| PD | Predicate disambiguation | batch_69cd4ba795808190acc9124c98c6e40f |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:09 p.m.