Triple
T12897480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darrell Porter |
E308532
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Porter |
E395427
|
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: Porter | Statement: [Darrell Porter, familyName, Porter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Porter Context triple: [Darrell 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 common English surname borne by numerous notable individuals across fields such as politics, sports, arts, and science.
-
E.
Parker
Parker is a 2013 American crime thriller film starring Jason Statham as a professional thief who seeks revenge after being double-crossed by his crew.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9717f3fc48190b61c8f6f36cd0725 |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a55f98c08190b8910b1443841fa7 |
completed | May 3, 2026, 1:31 a.m. |
Created at: April 9, 2026, 5:40 p.m.