Triple
T26896746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | World of Frozen |
E677916
|
entity |
| Predicate | containsShop |
P23299
|
FINISHED |
| Object | Tick Tock Toys & Collectibles |
—
|
NE NERFINISHED |
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: Tick Tock Toys & Collectibles | Statement: [World of Frozen, containsShop, Tick Tock Toys & Collectibles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsShop Context triple: [World of Frozen, containsShop, Tick Tock Toys & Collectibles]
-
A.
hasShop
chosen
Indicates that one entity owns, operates, or is associated with a shop or retail establishment.
-
B.
hasShopsOn
Indicates that one entity (typically a street, area, or building) contains or is lined with shops located on or along it.
-
C.
hasShopsOfType
Indicates that an entity contains or is associated with one or more shops belonging to a specified type or category.
-
D.
hasShopCount
Indicates the number of shops associated with a given entity.
-
E.
hasShopName
Indicates that an entity (such as a shop or business) is associated with a specific shop name.
- 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_69eee9befee48190a26f214faa867be7 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69fcda3699948190adb57625bae08091 |
completed | May 7, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69fcd8fd16d08190b0aca6e19a632e99 |
completed | May 7, 2026, 6:25 p.m. |
Created at: April 27, 2026, 5:48 a.m.