Triple
T74731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Libya |
E1495
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Tripoli |
E12108
|
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: Tripoli | Statement: [Libya, largestCity, Tripoli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tripoli Context triple: [Libya, largestCity, Tripoli]
-
A.
Tripoli
chosen
Tripoli is a historic Mediterranean port city that serves as the capital and largest urban center of Libya.
-
B.
Algiers
Algiers is the capital and largest city of Algeria, a major political, economic, and cultural center on the Mediterranean coast of North Africa.
-
C.
Tunis
Tunis is the capital and largest city of Tunisia, serving as a major political, economic, and cultural center in the Arab world.
-
D.
Beirut
Beirut is the capital and largest city of Lebanon, known as a historic cultural, commercial, and financial hub of the Eastern Mediterranean.
-
E.
Mogadishu
Mogadishu is the capital and largest city of Somalia, serving as a major political, economic, and cultural center on the Horn of Africa.
- 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_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a24f1b99a48190aec004ecd49b4a0d |
completed | Feb. 28, 2026, 2:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2aa42201881909b236a42e982a696 |
completed | Feb. 28, 2026, 8:41 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.