Triple
T9741369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Foursquare |
E236192
|
entity |
| Predicate | hasNumberOfUsers |
P22398
|
FINISHED |
| Object | tens of millions of registered users |
—
|
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: tens of millions of registered users | Statement: [Foursquare, hasNumberOfUsers, tens of millions of registered users]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfUsers Context triple: [Foursquare, hasNumberOfUsers, tens of millions of registered users]
-
A.
userCount
chosen
Indicates the number of users associated with or involved in a given context or entity.
-
B.
hasUser
Indicates that an entity is associated with or linked to a specific user.
-
C.
hasTenants
Indicates that an entity occupies or rents space from another entity as its tenant.
-
D.
hasPlayerCount
Indicates the number of players associated with or participating in a given entity or activity.
-
E.
hasUserService
Indicates that an entity is associated with or utilizes a particular user-related service.
- 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_69ca84d3e24481908a476e2231123cf9 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f2af3e48190b83a442cd0e84062 |
completed | April 1, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:23 p.m.