Triple
T363689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PENN Entertainment |
E7911
|
entity |
| Predicate | subsector |
P71
|
FINISHED |
| Object | casinos and gaming |
—
|
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: casinos and gaming | Statement: [PENN Entertainment, subsector, casinos and gaming]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subsector Context triple: [PENN Entertainment, subsector, casinos and gaming]
-
A.
sector
chosen
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
B.
subDisciplineOf
Indicates that one discipline is a more specialized or narrower field within another, broader discipline.
-
C.
sectorServed
Indicates the industry or economic sector that an entity primarily serves or targets with its activities, products, or services.
-
D.
subfamily
Indicates that one taxonomic group is a subfamily within a larger family, representing an intermediate rank in biological classification.
-
E.
subunitType
Indicates that one entity is a specific kind or classification of subunit within the structure or composition of another entity.
- 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.