Triple
T35977158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garde-Meuble de la Couronne |
E1040451
|
entity |
| Predicate | buildingLaterKnownAs |
P144948
|
FINISHED |
| Object | Hôtel de la Marine |
—
|
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: Hôtel de la Marine | Statement: [Garde-Meuble de la Couronne, buildingLaterKnownAs, Hôtel de la Marine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: buildingLaterKnownAs Context triple: [Garde-Meuble de la Couronne, buildingLaterKnownAs, Hôtel de la Marine]
-
A.
hasFormerStreetName
Indicates that an entity (such as a street or place) was previously known by a different street name.
-
B.
capitalLaterKnownAs
Indicates that an entity served as a capital city under one name at a given time and was later known by a different name.
-
C.
laterBecameKnownAs
chosen
Indicates that an entity was previously known by one name or identity and, at a later time, came to be known by a different name or identity.
-
D.
estateFormerName
Indicates that an estate previously had a different name than the one it currently holds.
-
E.
formerlyKnownLocationAs
Indicates that an entity was previously known or referred to by a different location name or designation.
- 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_69f76e27758c81909b711cf38a130aaf |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7bbf906d8819099020e548dd56bc9 |
completed | May 3, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a2dcf88190a7c9e109e41267be |
completed | May 3, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:07 p.m.