Triple
T36346051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Seine à Pont-de-l'Arche |
E895066
|
entity |
| Predicate | subjectLocationCountry |
P195019
|
FINISHED |
| Object | France |
—
|
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: France | Statement: [La Seine à Pont-de-l'Arche, subjectLocationCountry, France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectLocationCountry Context triple: [La Seine à Pont-de-l'Arche, subjectLocationCountry, France]
-
A.
subjectLocation
Indicates that one entity is located at, in, or near the place or position specified by another entity.
-
B.
primaryLocationCountry
Indicates the country that serves as the main or primary location associated with the subject.
-
C.
locationCountryAtTheTime
Indicates that an entity was located in a specified country during a particular time or time period.
-
D.
representsLocationInCountry
Indicates that one entity is located within the geographical boundaries of a specified country.
-
E.
currentLocationCountry
Indicates that one entity is the country in which another entity is currently located.
- F. None of above. chosen
Provenance (4 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_69f76e4f437c8190a1af3ea2564f41f5 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd974d75e08190af46b1d608769f3b |
completed | May 8, 2026, 7:57 a.m. |
| PD | Predicate disambiguation | batch_69fd94ff792c8190bedf4a639d3da809 |
completed | May 8, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69fd974c0e8481909fdd312897c647b3 |
completed | May 8, 2026, 7:57 a.m. |
Created at: May 3, 2026, 4:09 p.m.