Triple
T14604717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamerlingh Onnes |
E342797
|
entity |
| Predicate | hasEponymField |
P115019
|
FINISHED |
| Object | physics |
—
|
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: physics | Statement: [Kamerlingh Onnes, hasEponymField, physics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEponymField Context triple: [Kamerlingh Onnes, hasEponymField, physics]
-
A.
hasEponymConnectionTo
Indicates that one entity is named after, derived from, or otherwise linguistically or honorifically connected to another entity as its eponym.
-
B.
isNamedForEponymRole
Indicates that one entity bears a name derived from another entity that serves as its eponym or namesake.
-
C.
hasEponymFamilyRelation
Indicates that one entity is named after another entity to which it is related by family or kinship.
-
D.
hasOnomasticField
Indicates a relationship where something is associated with a specific onomastic field, i.e., a domain or category related to names or naming conventions.
-
E.
hasEponymSpouse
Indicates that one entity has a spouse after whom it is named or whose name it bears.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb44bf67c8190b4c48a7715f9443e |
completed | April 14, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69de656f9f4c81909f815b6629a9ee39 |
completed | April 14, 2026, 4:03 p.m. |
| PDg | Predicate description generation | batch_69de716c17cc8190aeb85296abee85a7 |
completed | April 14, 2026, 4:55 p.m. |
Created at: April 10, 2026, 1:25 a.m.