Triple
T5880189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Russell Conwell |
E130726
|
entity |
| Predicate | numberOfAcresOfDiamondsLectures |
P23576
|
FINISHED |
| Object | over 6,000 |
—
|
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 6,000 | Statement: [Russell Conwell, numberOfAcresOfDiamondsLectures, over 6,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAcresOfDiamondsLectures Context triple: [Russell Conwell, numberOfAcresOfDiamondsLectures, over 6,000]
-
A.
lecturesHeldIn
Indicates that a lecture event takes place or is conducted within a specific location or venue.
-
B.
hasNumberOfLessons
chosen
Indicates the specific count of lessons associated with an entity.
-
C.
gaveLecturesAt
Indicates that a person delivered lectures or taught courses at a particular institution or location.
-
D.
hasNumberOfAcres
Indicates the specific quantity of land area, measured in acres, that is associated with an entity.
-
E.
lectureSeries
Indicates a relationship where a set of lectures is organized and presented as a coherent, thematically linked series.
- F. None of above.
Provenance (3 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_69c0085523688190bfd487479ce819e6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0432fea5881909f5c291dd8db6105 |
completed | March 22, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69c033499ca08190bd26cee5b03f6306 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:57 p.m.