Triple
T21042148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr Sloane |
E518351
|
entity |
| Predicate | blackmails |
P62416
|
FINISHED |
| Object | Ed |
—
|
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: Ed | Statement: [Mr Sloane, blackmails, Ed]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ed Context triple: [Mr Sloane, blackmails, Ed]
-
A.
Ed
Ed is a small locality in western Sweden that serves as the administrative center of Dals-Ed Municipality in Västra Götaland County.
-
B.
Ed
Ed is an American television comedy-drama series that follows a lawyer who returns to his hometown to run a bowling alley while practicing law.
-
C.
Ed
chosen
Ed is a common masculine given name, typically used as a short form of names such as Edward, Edwin, or Edmund.
-
D.
ED
ED is the standard abbreviation for the Eredivisie, the top professional football league in the Netherlands.
-
E.
ED
ED is a classic line-based text editor commonly used in Unix-like operating systems, known for its minimal interface and suitability for scripting and low-resource environments.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcf0b27881909d1c5b58be387a74 |
completed | April 21, 2026, 4:28 a.m. |
Created at: April 16, 2026, 2:15 p.m.