Triple
T33955974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rose d'Or |
E870572
|
entity |
| Predicate | hasLocationHistory |
P8341
|
FINISHED |
| Object | Montreux |
—
|
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: Montreux | Statement: [Rose d'Or, hasLocationHistory, Montreux]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocationHistory Context triple: [Rose d'Or, hasLocationHistory, Montreux]
-
A.
tracksLocation
Indicates that one entity monitors and records the geographic position or movement of another entity over time.
-
B.
hasTourHistory
Indicates that an entity has a record of past tours or tour-related activities.
-
C.
historicalLocationOf
Indicates that a place served as a significant site or setting for an entity during a particular historical period or event.
-
D.
historicalPresence
chosen
Indicates that an entity existed, was active, or had a notable presence in a particular place or context during a past time period.
-
E.
hasTourHistoryWith
Indicates that two entities have previously participated together in one or more tours or touring events.
- 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_69f3499c2d7481909c953a5010227725 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69feba0f09508190b3e871c62b19ec7f |
completed | May 9, 2026, 4:37 a.m. |
| PD | Predicate disambiguation | batch_69feb957fe7c8190969fb31a6d1a59c8 |
completed | May 9, 2026, 4:34 a.m. |
Created at: May 1, 2026, 1:49 a.m.