Triple
T14313272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Mall at Rockingham Park |
E354887
|
entity |
| Predicate | hasApproximateRetailStoresCount |
P8902
|
FINISHED |
| Object | over 100 |
—
|
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: over 100 | Statement: [The Mall at Rockingham Park, hasApproximateRetailStoresCount, over 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateRetailStoresCount Context triple: [The Mall at Rockingham Park, hasApproximateRetailStoresCount, over 100]
-
A.
numberOfStores
chosen
Indicates the total count of stores associated with a given entity or context.
-
B.
hasRetailStores
Indicates that an entity operates or possesses one or more physical retail store locations.
-
C.
hasRetailPresenceIn
Indicates that an entity conducts retail operations or maintains a retail outlet, store, or sales presence within a specified location.
-
D.
hasRetailBoutiquesIn
Indicates that an entity operates or maintains retail boutiques located within a specified place or region.
-
E.
numberOfRestaurantsAndRetail
Indicates the total count of entities that are either restaurants or retail establishments associated with a given subject.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de85b49e5481909b9ffab2d922e284 |
completed | April 14, 2026, 6:21 p.m. |
| PD | Predicate disambiguation | batch_69de2a8f81f08190af737e1654847aa6 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:12 a.m.