Triple
T13454236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Korydallos |
E311186
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Keratsini |
E944292
|
NE FINISHED |
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: Keratsini | Statement: [Korydallos, locatedNear, Keratsini]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keratsini Context triple: [Korydallos, locatedNear, Keratsini]
-
A.
Keratsini
chosen
Keratsini is a coastal suburban municipality and port town within the greater Athens metropolitan area in Greece.
-
B.
Keratsini-Drapetsona
Keratsini-Drapetsona is a coastal municipality in the Athens urban area of Greece, known for its port-related industry and working-class neighborhoods.
-
C.
Keratea
Keratea is a town in eastern Attica, Greece, known for its historical significance and proximity to the Athens metropolitan area.
-
D.
Kerium
Kerium is a specialized hair and scalp care product line from La Roche-Posay designed to address issues such as dandruff, sensitivity, and hair thinning.
-
E.
Kera
Kera is a regional dialect of the Mundari language spoken by sections of the Munda ethnic community in eastern India.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d806a938b8819097ec43a2229fc7f9 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaefc52448190b30d7999f44a9765 |
completed | April 12, 2026, 2:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7399e33008190b10c14f30ff0c0d2 |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 9, 2026, 9:41 p.m.