Triple
T8282938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harlan |
E193721
|
entity |
| Predicate | associatedWithPerson |
P37
|
FINISHED |
| Object | Harlan Coben |
E663492
|
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: Harlan Coben | Statement: [Harlan, associatedWithPerson, Harlan Coben]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harlan Coben Context triple: [Harlan, associatedWithPerson, Harlan Coben]
-
A.
Harlan Coben
chosen
Harlan Coben is an American mystery and thriller novelist known for his twist-filled plots and bestselling standalone and series crime novels.
-
B.
Andrew Gross
Andrew Gross is an American author best known for his bestselling thrillers and for coauthoring several novels with James Patterson.
-
C.
Jeff Lindsay
Jeff Lindsay is an American novelist best known for creating the character Dexter Morgan in the crime thriller series that inspired the television show "Dexter."
-
D.
Chris Bohjalian
Chris Bohjalian is an American novelist known for his bestselling psychological and literary thrillers, several of which have been adapted for film and television.
-
E.
Nathan Barr
Nathan Barr is an American film and television composer known for his atmospheric scores on projects ranging from horror films to acclaimed series like True Blood and The Americans.
- 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_69ca82e217a48190880695635c44b2ed |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7aceec8881909cdfa488dfedc0f5 |
completed | March 31, 2026, 7:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd6877b8e481908ec1e0b91a5276f6 |
completed | April 1, 2026, 6:48 p.m. |
Created at: March 30, 2026, 5:51 p.m.