Triple
T16875576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edward Black |
E421289
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Oh, Mr Porter! |
E1139878
|
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: Oh, Mr Porter! | Statement: [Edward Black, notableWork, Oh, Mr Porter!]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oh, Mr Porter! Context triple: [Edward Black, notableWork, Oh, Mr Porter!]
-
A.
Oh, Mr Porter!
chosen
Oh, Mr Porter! is a 1937 British comedy film starring Will Hay, celebrated for its slapstick humor and status as a classic of British cinema.
-
B.
MR PORTER
MR PORTER is a luxury online retail destination specializing in high-end menswear, accessories, and lifestyle products from leading designer brands.
-
C.
Porter
Porter is a small town in Wagoner County, Oklahoma, known for its agricultural roots and annual peach festival.
-
D.
Porter
Porter is a common English occupational surname historically given to gatekeepers or doorkeepers.
-
E.
Porter
Porter is a transit station in Cambridge, Massachusetts that serves both MBTA commuter rail and Red Line subway services.
- 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_69d889d470fc8190b4aec199636c0c56 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3b7f646308190b5e277b5f51cd315 |
completed | April 18, 2026, 4:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c2b4abd08190841c5bb0b0eaa177 |
completed | May 10, 2026, 5:39 p.m. |
Created at: April 10, 2026, 5:29 a.m.