Triple
T19608914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | De Ruyter family |
E470677
|
entity |
| Predicate | hasFamilyOccupation |
P96643
|
FINISHED |
| Object | naval officer |
—
|
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: naval officer | Statement: [De Ruyter family, hasFamilyOccupation, naval officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamilyOccupation Context triple: [De Ruyter family, hasFamilyOccupation, naval officer]
-
A.
parentOccupation
Indicates that one entity has an occupation which is the job or profession of the other entity’s parent.
-
B.
siblingOccupation
Indicates that one person has a sibling whose job or profession is the specified occupation.
-
C.
hasFamilyRole
Indicates that one entity holds a specific familial role or position in relation to another entity.
-
D.
familyProfession
chosen
Indicates that a person’s profession is shared with or traditionally practiced within their family, reflecting an occupational lineage or family trade.
-
E.
hasFamilyRelationInWork
Indicates that there exists a family relationship between two entities within the context of a specific work (e.g., book, film, or other creative work).
- 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_69d8e510fa248190b7afb274a1d4cf73 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640c964fc8190bd1cb60f4b233eaa |
completed | April 20, 2026, 3:05 p.m. |
| PD | Predicate disambiguation | batch_69e514e166dc8190a0f147e0b4c8bbe7 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:43 p.m.