Triple
T18249284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sidirokastro |
E437040
|
entity |
| Predicate | locatedNorthOf |
P305
|
FINISHED |
| Object | Serres |
—
|
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: Serres | Statement: [Sidirokastro, locatedNorthOf, Serres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serres Context triple: [Sidirokastro, locatedNorthOf, Serres]
-
A.
Serres
chosen
Serres is a historic city in northern Greece known for its Byzantine heritage and role as a regional economic and cultural center.
-
B.
Serres
Serres is a French surname most notably associated with the philosopher and historian of science Michel Serres.
-
C.
San Javier
San Javier is a Chilean town known for its agricultural activity and wine production in the Maule Region.
-
D.
San Javier
San Javier is a municipality in Spain’s Region of Murcia, known for hosting the Spanish Air and Space Force’s main officer training academy and its nearby coastal and lagoon areas on the Mar Menor.
-
E.
San Javier
San Javier is a town in the Mexican state of Baja California Sur, known for its historic mission and role as a regional cultural and religious center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4fd7fa3708190baefd8d938d20807 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 10:33 a.m.