Triple
T3451710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edouard Heuer |
E72806
|
entity |
| Predicate | hasNotableInnovation |
P48898
|
FINISHED |
| Object | improvements in chronograph accuracy |
—
|
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: improvements in chronograph accuracy | Statement: [Edouard Heuer, hasNotableInnovation, improvements in chronograph accuracy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableInnovation Context triple: [Edouard Heuer, hasNotableInnovation, improvements in chronograph accuracy]
-
A.
hasNotableFeature
Indicates that an entity possesses a specific characteristic, trait, or attribute that is considered significant or noteworthy.
-
B.
hasGenreInnovation
Indicates that something introduces a novel or pioneering approach within its genre or category.
-
C.
hasNotableImpact
Indicates that one entity exerts a significant or noteworthy influence or effect on another entity or context.
-
D.
hasNotableCompany
Indicates that an entity is associated with or linked to a company that is considered notable or significant in some context.
-
E.
hasNotableIssue
Indicates that an entity is associated with a significant problem, concern, or defect that is noteworthy or exceptional compared to typical cases.
- 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_69ad85b12a908190a1d10a6b03b4f8ae |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adba7465248190947f9096e230e1c4 |
completed | March 8, 2026, 6:05 p.m. |
| PD | Predicate disambiguation | batch_69adae0255b48190a9069f7871c7a012 |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb1ecb02881908394f197e31431b4 |
completed | March 8, 2026, 5:29 p.m. |
Created at: March 8, 2026, 3:16 p.m.