Triple
T17602764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of the Peloponnese |
E428744
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Tripoli |
—
|
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: Tripoli | Statement: [University of the Peloponnese, city, Tripoli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tripoli Context triple: [University of the Peloponnese, city, Tripoli]
-
A.
Tripoli
Tripoli is a historic Mediterranean port city that serves as the capital and largest urban center of Libya.
-
B.
Tripoli
Tripoli is Lebanon’s second-largest city, a historic Mediterranean port known for its medieval Mamluk architecture and vibrant commercial life.
-
C.
Tripoli
Tripoli was a historic American shipyard and port city involved in constructing naval vessels such as the USS Intrepid.
-
D.
Tripoli
chosen
Tripoli is a historic city in the central Peloponnese of Greece that serves as the main urban and administrative center of the Arcadia region.
-
E.
مدينة بنغازي
مدينة بنغازي هي ثاني أكبر مدن ليبيا ومركز اقتصادي وثقافي مهم يقع في إقليم برقة على ساحل البحر المتوسط.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c49dbe081909bd39879c70fe27c |
completed | April 19, 2026, 5:46 a.m. |
Created at: April 10, 2026, 5:51 a.m.