Triple
T23164091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hess's |
E578666
|
entity |
| Predicate | peakNumberOfStores |
P125422
|
FINISHED |
| Object | over 70 |
—
|
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 70 | Statement: [Hess's, peakNumberOfStores, over 70]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakNumberOfStores Context triple: [Hess's, peakNumberOfStores, over 70]
-
A.
numberOfStores
Indicates the total count of stores associated with a given entity or context.
-
B.
numberOfRestaurantsAndRetail
Indicates the total count of entities that are either restaurants or retail establishments associated with a given subject.
-
C.
numberOfLocationsAtPeak
chosen
Indicates the total count of distinct locations associated with an entity at its highest or peak point in time or activity.
-
D.
hasRetailStores
Indicates that an entity operates or possesses one or more physical retail store locations.
-
E.
numberOfFloorsInAnchorStores
Indicates the relationship specifying how many floors are contained within each anchor store.
- 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_69e245fc75348190a0288401044c8af8 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18f2b0414819081f9c2d6b8c88421 |
completed | April 29, 2026, 4:55 a.m. |
| PD | Predicate disambiguation | batch_69ef89ff76808190808ee4ad9dea776b |
completed | April 27, 2026, 4:08 p.m. |
Created at: April 17, 2026, 4:02 p.m.