Triple
T35758326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hockenheimring |
E1033504
|
entity |
| Predicate | regularF1UseFrom |
P138017
|
FINISHED |
| Object | 1977 |
—
|
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: 1977 | Statement: [Hockenheimring, regularF1UseFrom, 1977]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regularF1UseFrom Context triple: [Hockenheimring, regularF1UseFrom, 1977]
-
A.
regularF1HostFrom
Indicates that an entity regularly serves as the host location for Formula 1 events originating from a specified place or jurisdiction.
-
B.
regularJockey
Indicates that a person routinely serves as the jockey for a particular horse in races or training.
-
C.
hasRacingUseSince
chosen
Indicates that an entity has been used for racing starting from a specified point in time.
-
D.
isFrequently
Indicates that an action, state, or relationship occurs often or with high regularity between the related entities.
-
E.
areRegularIn
Indicates that entities participate in or occur within a context, pattern, or structure in a consistent, uniform, and rule-governed manner.
- 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_69f76e1262f48190a313318665acc189 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fda94697c4819081291967202248be |
completed | May 8, 2026, 9:13 a.m. |
| PD | Predicate disambiguation | batch_69fda5973fcc8190a57daef31fb70a49 |
completed | May 8, 2026, 8:57 a.m. |
Created at: May 3, 2026, 4:06 p.m.