Triple
T35142576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ursula Buffay |
E1014733
|
entity |
| Predicate | crossOverCharacterBetween |
P189748
|
FINISHED |
| Object | Friends and Mad About You |
—
|
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: Friends and Mad About You | Statement: [Ursula Buffay, crossOverCharacterBetween, Friends and Mad About You]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crossOverCharacterBetween Context triple: [Ursula Buffay, crossOverCharacterBetween, Friends and Mad About You]
-
A.
crossesOverWith
Indicates that one entity intersects or overlaps with another, typically by passing across or through its path, boundary, or extent.
-
B.
crossedBy
Indicates that one entity (typically a path, line, or boundary) is intersected or traversed by another entity.
-
C.
crossingOf
Indicates that one entity serves as the intersection or crossing point of two or more linear features, such as roads, paths, or tracks.
-
D.
crossWith
Indicates that one entity intersects or passes over/through the path, boundary, or position of another entity.
-
E.
crossPosition
Indicates a spatial relationship where one entity is positioned so that it crosses over or intersects the position or path of another entity.
- 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_69f76dda7c108190a2ffd93eb6c341a7 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fbca6c066c8190a1599202f341417f |
completed | May 6, 2026, 11:10 p.m. |
| PD | Predicate disambiguation | batch_69fbc8ec03ac8190a757563f96fab283 |
completed | May 6, 2026, 11:04 p.m. |
| PDg | Predicate description generation | batch_69fbc9d0854c8190aa00093274afebb8 |
completed | May 6, 2026, 11:08 p.m. |
Created at: May 3, 2026, 4:02 p.m.