Triple
T18156350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Casino Square |
E434639
|
entity |
| Predicate | roleInLap |
P130665
|
FINISHED |
| Object | early-to-mid lap section of Monaco Grand Prix circuit |
—
|
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: early-to-mid lap section of Monaco Grand Prix circuit | Statement: [Casino Square, roleInLap, early-to-mid lap section of Monaco Grand Prix circuit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInLap Context triple: [Casino Square, roleInLap, early-to-mid lap section of Monaco Grand Prix circuit]
-
A.
roleInvolves
Indicates that a particular role includes or requires participation in a specified activity, responsibility, or function.
-
B.
roleInFrame
Indicates that an entity participates in a specific semantic frame by filling a particular role within that frame.
-
C.
roleInMovement
Indicates the specific function, position, or contribution an entity has within a broader movement or collective effort.
-
D.
roleInEngine
Indicates the specific function or responsibility an entity has within an engine or engine-like system.
-
E.
roleInRepertoire
Indicates that an entity serves a specific role or function within a larger repertoire, collection, or set of items.
- 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_69d8b90b7a188190b3fc7b8d4a6cd20a |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4debe27a88190bd76c6f78fcf1bd1 |
completed | April 19, 2026, 1:55 p.m. |
| PD | Predicate disambiguation | batch_69e43317d11c81908d1dc14921566b47 |
completed | April 19, 2026, 1:42 a.m. |
| PDg | Predicate description generation | batch_69e438f5ae2c8190b11dee46534fa5a9 |
completed | April 19, 2026, 2:07 a.m. |
Created at: April 10, 2026, 10:30 a.m.