Triple
T31720625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Torcy |
E809567
|
entity |
| Predicate | twinningCountry |
P52852
|
FINISHED |
| Object | Germany |
—
|
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: Germany | Statement: [Torcy, twinningCountry, Germany]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: twinningCountry Context triple: [Torcy, twinningCountry, Germany]
-
A.
hasTwinTown
Indicates that two towns or cities are officially paired in a twinning relationship, typically for cultural, social, or economic exchange.
-
B.
countryPartner
Indicates a formal partnership relationship between two countries, such as cooperation, alliance, or strategic collaboration.
-
C.
relatedCountry
Indicates that there is a relevant or associated relationship between an entity and a specified country, without specifying the exact nature of that relationship.
-
D.
sisterCityCountry
chosen
Indicates that a city has an official sister-city relationship with a city located in the specified country.
-
E.
sisterMunicipalityWith
Indicates a formal partnership or twinning relationship between two municipalities, typically for cultural, social, or economic cooperation.
- 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_69f348e009c8819095d77df52c645b9c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6aaf80e688190b2ca6b676745bff6 |
completed | May 3, 2026, 1:55 a.m. |
| PD | Predicate disambiguation | batch_69f6aa20a1588190a53533fc9764efb2 |
completed | May 3, 2026, 1:51 a.m. |
Created at: April 30, 2026, 11:18 p.m.