Triple
T33738960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Farrell |
E864506
|
entity |
| Predicate | hasOnScreenNeighbour |
P184415
|
FINISHED |
| Object | Suze Littlewood |
—
|
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: Suze Littlewood | Statement: [Tom Farrell, hasOnScreenNeighbour, Suze Littlewood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOnScreenNeighbour Context triple: [Tom Farrell, hasOnScreenNeighbour, Suze Littlewood]
-
A.
hasNeighboringObject
Indicates that one object is located adjacent to or directly next to another object in space.
-
B.
onScreenSibling
Indicates that two characters are depicted as siblings within an on-screen or fictional context.
-
C.
hasNeighbouringUnit
Indicates that one unit is directly adjacent to and shares a boundary or side with another unit.
-
D.
hasOnScreenRelative
Indicates that one entity has a family member who appears or is depicted on screen in relation to it.
-
E.
hasNeighbouringQuarter
Indicates that one quarter (district or area) is directly adjacent to and shares a boundary with another quarter.
- 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_69f3498b24b8819096a65009e521d0e1 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69f7b0e3917481908a394680d76743c3 |
completed | May 3, 2026, 8:32 p.m. |
Created at: May 1, 2026, 1:44 a.m.