Triple
T24876189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Kathryn La Vallette |
E622575
|
entity |
| Predicate | hasFamilyNavalLegacy |
P20564
|
FINISHED |
| Object | La Vallette family |
—
|
NE NERFINISHED |
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: La Vallette family | Statement: [Miss Kathryn La Vallette, hasFamilyNavalLegacy, La Vallette family]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamilyNavalLegacy Context triple: [Miss Kathryn La Vallette, hasFamilyNavalLegacy, La Vallette family]
-
A.
hasNavalRole
Indicates that an entity holds or is assigned a role, function, or duty within a naval or maritime context.
-
B.
hasNavalComponent
Indicates that something includes, involves, or is associated with a naval or maritime element as part of its composition or structure.
-
C.
hasNotableNavalRank
Indicates that an entity holds or has held a distinguished or significant rank within a naval organization.
-
D.
hasMaritimeHeritage
chosen
Indicates that an entity possesses a historical, cultural, or traditional connection to maritime activities, seafaring, or the sea.
-
E.
hadNavalInfantry
Indicates that a military force possessed or was supported by specialized naval infantry (marines) as part of its organization or operations.
- 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_69e2fac3fdbc81909c2ec49be5743cd9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f606c79ad081908369605f72e65ca6 |
completed | May 2, 2026, 2:14 p.m. |
| PD | Predicate disambiguation | batch_69f602ce79ec8190b8336c2b9de18ac7 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 18, 2026, 5:24 a.m.