Triple
T28214876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Genevieve Lacasse |
E711283
|
entity |
| Predicate | caught |
P5909
|
FINISHED |
| Object | left |
—
|
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: left | Statement: [Genevieve Lacasse, caught, left]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caught Context triple: [Genevieve Lacasse, caught, left]
-
A.
caughtBetween
Indicates being simultaneously subject to opposing forces, demands, or sides, unable to fully align with or escape either.
-
B.
trap
Indicates that an entity captures, confines, or ensnares another entity, typically preventing its escape or movement.
-
C.
catches
chosen
Indicates that one entity successfully seizes, intercepts, or takes hold of another entity, often stopping its motion or preventing its escape.
-
D.
held
Indicates that one entity physically grasped, carried, or kept another entity in its possession or control.
-
E.
encountered
Indicates that one entity came across or met another entity, typically in a specific place or context, often unexpectedly or during the course of some activity.
- F. None of above.
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_69efb51cb5288190818c1f63a266af11 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f6434bfb4881909c3309b3ec88c17a |
completed | May 2, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69f63c6c1a948190b68c0f92c264cc0c |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 10:42 p.m.