Triple
T7731182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ben Rogers |
E175258
|
entity |
| Predicate | firstPublicationYearOfAppearance |
P78407
|
FINISHED |
| Object | 1876 |
—
|
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: 1876 | Statement: [Ben Rogers, firstPublicationYearOfAppearance, 1876]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstPublicationYearOfAppearance Context triple: [Ben Rogers, firstPublicationYearOfAppearance, 1876]
-
A.
firstAppearedAt
Indicates the point in time or specific event at which an entity was first introduced, observed, or became known.
-
B.
firstPopularAppearance
Indicates the earliest notable or widely recognized appearance of an entity in a public or popular context.
-
C.
firstAppearanceFor
Indicates that an entity marks the initial occurrence or debut of another entity within a given context or medium.
-
D.
settingOfFirstAppearance
Indicates the location or context in which an entity is first introduced or appears.
-
E.
firstAppearanceFranchise
Indicates the franchise in which an entity made its first appearance.
- 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_69c6995e912c81909a49a2657103f786 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70402169481909b219dc5f4a64b9b |
completed | March 27, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69c7016c4a748190a7012030edaefcee |
completed | March 27, 2026, 10:15 p.m. |
| PDg | Predicate description generation | batch_69c7040091608190a9e46ecfb2ff0bca |
completed | March 27, 2026, 10:26 p.m. |
Created at: March 27, 2026, 4:06 p.m.