Triple
T8799956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anytime Fitness |
E209378
|
entity |
| Predicate | clubSize |
P84696
|
FINISHED |
| Object | small to medium-sized facilities |
—
|
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: small to medium-sized facilities | Statement: [Anytime Fitness, clubSize, small to medium-sized facilities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clubSize Context triple: [Anytime Fitness, clubSize, small to medium-sized facilities]
-
A.
typicalGroupSize
Indicates the usual or characteristic number of individuals that typically form a group in this context.
-
B.
communitySize
Indicates the number of individuals or units that make up a given community.
-
C.
typicalGroupSizeRange
Indicates the usual minimum and maximum number of individuals that typically occur together in a group for the given entity.
-
D.
staffSize
Indicates the number of staff members associated with an entity.
-
E.
guestCountApproximate
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
- F. None of above. chosen
Provenance (4 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_69ca836320e48190b5cf585b90a322c4 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fb7491c8190bcdb98d6cc003d9e |
completed | March 31, 2026, 11:58 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1f28ec8190a34311cb412920c2 |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5cff3608819081d2d7e5c16d44b7 |
completed | March 31, 2026, 11:47 p.m. |
Created at: March 30, 2026, 6:44 p.m.