Triple
T24931085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugh Grant as Will Freeman |
E618986
|
entity |
| Predicate | relationshipTypeWithMarcus Brewer |
P169505
|
FINISHED |
| Object | mentor-like |
—
|
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: mentor-like | Statement: [Hugh Grant as Will Freeman, relationshipTypeWithMarcus Brewer, mentor-like]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithMarcus Brewer Context triple: [Hugh Grant as Will Freeman, relationshipTypeWithMarcus Brewer, mentor-like]
-
A.
relationshipTypeWithMarkMcPherson
Indicates the specific nature or category of relationship that an entity has with Mark McPherson.
-
B.
relationshipTypeWithRogerDeBris
Indicates the specific nature or category of relationship that an entity has with Roger De Bris.
-
C.
relationshipTypeWithJohnLuther
Indicates the specific nature or category of the relationship an entity has with John Luther.
-
D.
relationshipTypeWithJoshSrebnick
Indicates the specific nature or category of the relationship that an entity has with Josh Srebnick.
-
E.
relationshipTypeWithSeanBoswell
Indicates the specific nature or category of relationship that an entity has with Sean Boswell.
- 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_69e2fab9edd88190b86004a78a28bc20 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f67fc237608190b6542b56038a7fe4 |
completed | May 2, 2026, 10:50 p.m. |
| PD | Predicate disambiguation | batch_69f67e3ed894819094c067c1ef624951 |
completed | May 2, 2026, 10:44 p.m. |
| PDg | Predicate description generation | batch_69f67f0353c88190a05b2db449abe0f4 |
completed | May 2, 2026, 10:47 p.m. |
Created at: April 18, 2026, 5:30 a.m.