Triple
T4417485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marquis de Lafayette statue (Paris, Cours-la-Reine) |
E95009
|
entity |
| Predicate | hasSubjectNationality |
P43801
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Marquis de Lafayette statue (Paris, Cours-la-Reine), hasSubjectNationality, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubjectNationality Context triple: [Marquis de Lafayette statue (Paris, Cours-la-Reine), hasSubjectNationality, French]
-
A.
bearerNationality
Indicates that one entity is the country or nationality associated with the bearer of another entity, such as a document or credential.
-
B.
workSubjectNationality
chosen
Indicates that the subject of a work has a specified nationality.
-
C.
appliesToPersonNationality
Indicates that something is relevant or applicable specifically to a person’s nationality.
-
D.
hasCitizenshipRestriction
Indicates that there is a legal or policy-based limitation on who can obtain or hold citizenship in a given context.
-
E.
includedNationality
Indicates that one entity’s set of nationalities contains or encompasses the nationality of another entity.
- 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_69b3453a36908190b95a79a297ca083c |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3551d5d7481908528c2de0a6fda06 |
completed | March 13, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69b34f5d0c54819085c08533bb58030a |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:29 p.m.