Triple
T7568960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Maître de France |
E179190
|
entity |
| Predicate | seatOrOfficeLocation |
P23175
|
FINISHED |
| Object | royal court of France |
—
|
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: royal court of France | Statement: [Grand Maître de France, seatOrOfficeLocation, royal court of France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatOrOfficeLocation Context triple: [Grand Maître de France, seatOrOfficeLocation, royal court of France]
-
A.
seatLocation
Indicates the spatial position or placement of a seat relative to a reference point or environment.
-
B.
seatLocatedAt
chosen
Indicates that a seat is positioned or situated at a specific location or place.
-
C.
seatLocatedIn
Indicates that a seat is situated within or belongs to a specific location or area.
-
D.
seatOftenLocatedIn
Indicates that one type of seat is commonly or typically found within a particular location or setting.
-
E.
seatOrDomain
Indicates a relationship where something serves as, or is associated with, the primary location, base, or jurisdictional area (seat or domain) of an 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_69c69f316e50819081a271c85c06f918 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f91ec780819099de6227a27bf5a5 |
completed | March 27, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69c6f4de77048190b8769e717fdcf8e7 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:51 p.m.