Triple
T363668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PENN Entertainment |
E7911
|
entity |
| Predicate | hasBusinessUnit |
P7588
|
FINISHED |
| Object | retail casinos |
—
|
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: retail casinos | Statement: [PENN Entertainment, hasBusinessUnit, retail casinos]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBusinessUnit Context triple: [PENN Entertainment, hasBusinessUnit, retail casinos]
-
A.
hasBusinessDivision
chosen
Indicates that an organization includes or is composed of a specific business division as a subordinate unit.
-
B.
hasAdministrativeUnit
Indicates that one entity possesses, contains, or is associated with another entity that functions as its administrative subdivision or governing unit.
-
C.
hasBusinessDistrict
Indicates that a place or administrative area contains or includes a designated business district within its boundaries.
-
D.
hasEconomicOrganization
Indicates that an entity possesses, is associated with, or participates in a specific economic organization or institutional economic structure.
-
E.
hasTypeOfOrganization
Indicates that an entity is classified as belonging to a particular type or category of organization.
- 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebd1016481909b8ba3b047a47145 |
completed | Feb. 28, 2026, 1:21 p.m. |
| PD | Predicate disambiguation | batch_69a2e95c843c8190b2aba9af6e869ba1 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.