Triple
T2325152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Publius |
E48270
|
entity |
| Predicate | JayContributionsCount |
P38105
|
FINISHED |
| Object | 5 essays (traditional attribution) |
—
|
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: 5 essays (traditional attribution) | Statement: [Publius, JayContributionsCount, 5 essays (traditional attribution)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: JayContributionsCount Context triple: [Publius, JayContributionsCount, 5 essays (traditional attribution)]
-
A.
hasContributionFrom
Indicates that something (such as a work, project, or outcome) is created, influenced, or supported in part by a specified contributor.
-
B.
bookmarkCount
Indicates the number of times an item has been bookmarked by users.
-
C.
registerCount
Indicates the number of registers associated with or allocated to a given entity in a system.
-
D.
tributeFrom
Indicates that one entity pays or provides tribute, often as a sign of submission or obligation, to another entity.
-
E.
userCount
Indicates the number of users associated with or involved in a given context or entity.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc685f05481909c863b29d1f6bacd |
completed | March 7, 2026, 6:32 a.m. |
| PD | Predicate disambiguation | batch_69abc5909cc48190aab257313542dc49 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abc682d094819081a96ffb77c4c42a |
completed | March 7, 2026, 6:32 a.m. |
Created at: March 4, 2026, 7:50 p.m.