Triple
T7243947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | On Heroes, Hero-Worship, and the Heroic in History |
E156425
|
entity |
| Predicate | yearLecturesDelivered |
P75976
|
FINISHED |
| Object | 1840 |
—
|
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: 1840 | Statement: [On Heroes, Hero-Worship, and the Heroic in History, yearLecturesDelivered, 1840]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearLecturesDelivered Context triple: [On Heroes, Hero-Worship, and the Heroic in History, yearLecturesDelivered, 1840]
-
A.
lecturesHeldIn
Indicates that a lecture event takes place or is conducted within a specific location or venue.
-
B.
gaveLecturesAt
Indicates that a person delivered lectures or taught courses at a particular institution or location.
-
C.
numberOfLectures
Indicates the total count of lectures associated with a given entity or context.
-
D.
yearWorkWritten
Indicates the year in which a particular work was written or created.
-
E.
notableLecturer
Indicates that a person is recognized as a distinguished or prominent lecturer, often due to their expertise, impact, or reputation in giving lectures.
- 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_69c68827b5e481908dc05e145b2c92d4 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea58533481909af7a4a6ade40eff |
completed | March 27, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69c6e7644648819096a5e2de5d0dbe97 |
completed | March 27, 2026, 8:24 p.m. |
| PDg | Predicate description generation | batch_69c6ea539f5c81908001524149903559 |
completed | March 27, 2026, 8:36 p.m. |
Created at: March 27, 2026, 2:56 p.m.