Triple
T920948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delicious |
E19880
|
entity |
| Predicate | bookmarkCount |
P22399
|
FINISHED |
| Object | over 180 million bookmarks 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 180 million bookmarks at its peak | Statement: [Delicious, bookmarkCount, over 180 million bookmarks at its peak]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bookmarkCount Context triple: [Delicious, bookmarkCount, over 180 million bookmarks at its peak]
-
A.
arrowCount
Indicates the number of arrows associated with or involved in a given entity or interaction.
-
B.
titleCount
Indicates the number of distinct titles associated with an entity within a given context.
-
C.
registerCount
Indicates the number of registers associated with or allocated to a given entity in a system.
-
D.
starCount
Indicates the number of stars associated with an entity, typically representing a rating, quality level, or count of starred items.
-
E.
mirrorCount
Indicates the number of mirrors associated with or present in relation to a given entity or context.
- 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.