Triple
T3058298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Aigaleo |
E60533
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Perama |
E306705
|
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: Perama | Statement: [Mount Aigaleo, near, Perama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Perama Context triple: [Mount Aigaleo, near, Perama]
-
A.
Perama
chosen
Perama is a coastal suburb and port town in the Athens urban area of Greece, known for its shipyards and maritime industry.
-
B.
Enyo
Enyo is a Greek goddess of war and destruction, often depicted as a close companion and counterpart to the war god Ares.
-
C.
Vipsania
Vipsania is an ancient Roman feminine praenomen (personal name) used within the gens Vipsania.
-
D.
Sheva
Sheva is a locality in Navi Mumbai, India, known primarily for its proximity to the major Jawaharlal Nehru Port (Nhava Sheva), one of the country’s busiest container ports.
-
E.
Citium
Citium was an ancient city on the southern coast of Cyprus, historically significant as a Phoenician-Greek trading center and the birthplace of the Stoic philosopher Zeno.
- 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_69ad8578137c81908259dcb27c7d6d7c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ad9e1741648190b710b7022252498d |
completed | March 8, 2026, 4:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1ef0903cc81909a073fe78dbf0b14 |
completed | March 11, 2026, 10:39 p.m. |
Created at: March 8, 2026, 3:02 p.m.