Triple
T17350143
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Every Girl Should Be Married |
E421785
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Harry Marker |
E456156
|
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: Harry Marker | Statement: [Every Girl Should Be Married, editedBy, Harry Marker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harry Marker Context triple: [Every Girl Should Be Married, editedBy, Harry Marker]
-
A.
Harry Marker
chosen
Harry Marker was a film editor best known for his work on classic Hollywood productions such as "The Bells of St. Mary's."
-
B.
John Marks
John Marks is an American author and journalist known for his investigative and political writing, including collaborations with fellow reporter Joseph Medill Patterson Albright.
-
C.
Alvin Marks
Alvin Marks was an American inventor known for his work on advanced energy technologies and high-efficiency lighting concepts.
-
D.
Marvin Eastman
Marvin Eastman is an American mixed martial artist and former professional kickboxer known for competing in the UFC’s light heavyweight division.
-
E.
Fritz Lanman
Fritz Lanman is an American technology executive and investor known for his leadership roles at companies like ClassPass and his early investment in and involvement with startups such as Square.
- 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_69d889d520008190a26917a95bf1c2ea |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a2bd0a881909e71c89773d9273c |
completed | April 19, 2026, 2:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0195585e5881909b0ad386b65112ba |
completed | May 11, 2026, 8:37 a.m. |
Created at: April 10, 2026, 5:44 a.m.