Triple
T24365016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jules Bianchi |
E614169
|
entity |
| Predicate | helmetNumberStatus |
P155931
|
FINISHED |
| Object | car number 17 retired in his honor |
—
|
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: car number 17 retired in his honor | Statement: [Jules Bianchi, helmetNumberStatus, car number 17 retired in his honor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: helmetNumberStatus Context triple: [Jules Bianchi, helmetNumberStatus, car number 17 retired in his honor]
-
A.
helmetNumber
Indicates the identifying number assigned to a person or object as displayed on their helmet.
-
B.
helmetType
Indicates the specific category or style of helmet associated with an entity.
-
C.
helmetColor
Indicates the specific color attribute assigned to a helmet in the relationship.
-
D.
woreHelmetOf
Indicates that one entity used or was equipped with the helmet that belongs to or is associated with another entity.
-
E.
wearsHelmetBrand
Indicates that an entity uses or is equipped with a helmet of a specified brand.
- 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_69e2d7dfe7f08190b7a1f3a36483ab05 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f293874f7c8190b472e99640e97f62 |
completed | April 29, 2026, 11:25 p.m. |
| PD | Predicate disambiguation | batch_69f287bb1b2c81909c2e7fcc392ad143 |
completed | April 29, 2026, 10:35 p.m. |
| PDg | Predicate description generation | batch_69f28f4d978c81908310c01def2514cc |
completed | April 29, 2026, 11:07 p.m. |
Created at: April 18, 2026, 2:01 a.m.