Triple
T17617464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter B. Porter |
E429121
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Porter |
—
|
NE NERFINISHED |
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: Porter | Statement: [Peter B. Porter, familyName, Porter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Porter Context triple: [Peter B. Porter, familyName, Porter]
-
A.
Porter
Porter is a transit station in Cambridge, Massachusetts that serves both MBTA commuter rail and Red Line subway services.
-
B.
Porter
chosen
Porter is a common English occupational surname historically given to gatekeepers or doorkeepers.
-
C.
Porter
Porter is a small town in Wagoner County, Oklahoma, known for its agricultural roots and annual peach festival.
-
D.
Parker
Parker is a suburban town in Colorado located along the eastern edge of the Denver metropolitan area.
-
E.
Parker
Parker is the troubled, tattoo-obsessed protagonist of Flannery O’Connor’s short story “Parker’s Back,” whose spiritual and personal turmoil drive the narrative.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46d33a2b081908deecee773c333af |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 5:51 a.m.