Triple
T19062809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tower ward |
E466577
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Mark Lane |
—
|
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: Mark Lane | Statement: [Tower ward, contains, Mark Lane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Lane Context triple: [Tower ward, contains, Mark Lane]
-
A.
Mark Lane
chosen
Mark Lane is a street in the City of London, historically associated with the grain trade and located near Fenchurch Street and the Tower of London.
-
B.
Mark Lane
Mark Lane is a film producer known for his work on genre movies, including the shark thriller "47 Meters Down: Uncaged."
-
C.
Martin Lane
Martin Lane is a central father figure and newspaper editor on the 1960s American sitcom "The Patty Duke Show."
-
D.
Richard Lane
Richard Lane was a co-founder of the British publishing house Penguin Books, which became famous for pioneering affordable, high-quality paperback editions.
-
E.
Richard Lane
Richard Lane was an American character actor and announcer known for his prolific work in film and early television, often playing fast-talking reporters or authority figures.
- 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_69d8dd040fb881909af2a964f65ad208 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e195ac048190a57b57e585d022d4 |
completed | April 20, 2026, 8:19 a.m. |
Created at: April 10, 2026, 12:03 p.m.