Triple
T31511586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Griff |
E803954
|
entity |
| Predicate | characterRoleOfLorneGreene |
P197096
|
FINISHED |
| Object | former police officer turned private investigator |
—
|
LITERAL 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: former police officer turned private investigator | Statement: [Griff, characterRoleOfLorneGreene, former police officer turned private investigator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterRoleOfLorneGreene Context triple: [Griff, characterRoleOfLorneGreene, former police officer turned private investigator]
-
A.
leadActorForCharacter David Greene
Indicates that the specified person is the primary actor portraying the character David Greene.
-
B.
characterVoicedBy Loren Lester
Indicates that a character is voiced by Loren Lester.
-
C.
leadActorForCharacterLance
Indicates that the referenced person is the primary actor who portrays the character named Lance.
-
D.
roleOfLanceReddick
Indicates that the specified role or character is portrayed by Lance Reddick.
-
E.
leadActorForCharacter Vince Grayson
Indicates that Vince Grayson is the primary actor portraying a particular character.
- F. None of above. chosen
Provenance (4 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_69f348ceb0a48190ae7feca263b6296c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe779248c081909f0ed1a2a0df23db |
completed | May 8, 2026, 11:53 p.m. |
| PD | Predicate disambiguation | batch_69fe76eaf6d48190998bc7168749cc42 |
completed | May 8, 2026, 11:51 p.m. |
| PDg | Predicate description generation | batch_69fe779167648190936bd49cc1049178 |
completed | May 8, 2026, 11:53 p.m. |
Created at: April 30, 2026, 9:50 p.m.