Triple
T6493153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Powell |
E148089
|
entity |
| Predicate | JohnWesleyPowellOccupation |
P12884
|
FINISHED |
| Object | explorer |
—
|
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: explorer | Statement: [Lake Powell, JohnWesleyPowellOccupation, explorer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: JohnWesleyPowellOccupation Context triple: [Lake Powell, JohnWesleyPowellOccupation, explorer]
-
A.
roleInThomasEdisonCareer
Indicates that one entity had a specific role or involvement in the course of Thomas Edison’s professional career.
-
B.
roleOfHenryWhitneyBellows
Indicates that the specified role, position, or office is held by Henry Whitney Bellows.
-
C.
notableHistoricalFigureRole
Indicates that an entity is recognized as a historically significant person who played a particular role or held a specific position in history.
-
D.
namedPersonOccupation
chosen
Indicates that a person is explicitly identified as having a particular occupation or job role.
-
E.
notableHolderOccupation
Indicates that a person notably associated with an entity (e.g., an award, office, or title) held a particular occupation or professional role.
- 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_69c009088f3081909cd467b05919de30 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06ab6abbc8190a4971ad5a654b0cd |
completed | March 22, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69c06740bebc81909d9d6956baa2bcb9 |
completed | March 22, 2026, 10:03 p.m. |
Created at: March 22, 2026, 4:53 p.m.