Triple
T920947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delicious |
E19880
|
entity |
| Predicate | userCount |
P22398
|
FINISHED |
| Object | over 5 million users at its peak |
—
|
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 5 million users at its peak | Statement: [Delicious, userCount, over 5 million users at its peak]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: userCount Context triple: [Delicious, userCount, over 5 million users at its peak]
-
A.
sessionCount
Indicates the number of distinct sessions associated with an entity or interaction context.
-
B.
registerCount
Indicates the number of registers associated with or allocated to a given entity in a system.
-
C.
numberOfSubscribers
Indicates the total count of subscribers associated with a given entity.
-
D.
circulationUsers
Indicates a relationship where users are involved in or affected by the circulation or lending of items within a system.
-
E.
visitorCount
Indicates the number of visitors associated with a particular entity, context, or time period.
- 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_69a493a099788190a696d9d8408cbaf4 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b388f0bc8190a087222636135ba5 |
completed | March 1, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69a4b2944ff88190a260be5355132ba5 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b385176081909e3e8c3f647c1fd4 |
completed | March 1, 2026, 9:45 p.m. |
Created at: March 1, 2026, 7:40 p.m.