Triple
T30264492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Louis Outlet Mall |
E769598
|
entity |
| Predicate | numberOfAnchorStoresAtPeak |
P61394
|
FINISHED |
| Object | several |
—
|
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: several | Statement: [St. Louis Outlet Mall, numberOfAnchorStoresAtPeak, several]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAnchorStoresAtPeak Context triple: [St. Louis Outlet Mall, numberOfAnchorStoresAtPeak, several]
-
A.
numberOfFloorsInAnchorStores
Indicates the relationship specifying how many floors are contained within each anchor store.
-
B.
numberOfLocationsAtPeak
Indicates the total count of distinct locations associated with an entity at its highest or peak point in time or activity.
-
C.
hasAnchorStores
chosen
Indicates that a retail property or shopping center includes one or more major anchor stores as primary tenants.
-
D.
memberCountAtPeak
Indicates the highest number of members that an entity (such as a group or organization) has had at any point in time.
-
E.
numberOfStores
Indicates the total count of stores associated with a given entity or 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_69f22484a5f48190b678cd607700bc82 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a0067cde0f08190b2cd93af5f00d519 |
completed | May 10, 2026, 11:11 a.m. |
| PD | Predicate disambiguation | batch_6a0065820c8c8190994734433c64a30a |
completed | May 10, 2026, 11:01 a.m. |
Created at: April 29, 2026, 7:42 p.m.