Triple
T9565383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dear John |
E230776
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Scott Porter |
E240306
|
NE 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: Scott Porter | Statement: [Dear John, starring, Scott Porter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scott Porter Context triple: [Dear John, starring, Scott Porter]
-
A.
Scott Porter
chosen
Scott Porter is an American actor best known for his roles on television series such as "Friday Night Lights" and "Hart of Dixie."
-
B.
Chris Porter
Chris Porter is a music producer best known for his work on the hit song "Back for Good" by Take That.
-
C.
Eric Porter
Eric Porter was a distinguished English actor best known for his classical stage work and prominent roles in British television and film during the mid-20th century.
-
D.
John Porter
John Porter is a British record producer and musician best known for his work on influential blues and rock albums.
-
E.
Ben Porterfield
Ben Porterfield is a technology entrepreneur best known as a co-founder of the business intelligence and data analytics company Looker.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca847f22188190a56e4a97625bef22 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd996a01b081908e2782f41520f73d |
completed | April 1, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d152abb0788190ab2e204d9a082ccf |
completed | April 4, 2026, 6:04 p.m. |
Created at: March 30, 2026, 8:04 p.m.