Triple
T6909581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julian March |
E159895
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Pula |
E262587
|
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: Pula | Statement: [Julian March, containsCity, Pula]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pula Context triple: [Julian March, containsCity, Pula]
-
A.
Pula
chosen
Pula is a historic coastal city in Croatia known for its well-preserved Roman amphitheater and Adriatic seaside location.
-
B.
Kwaluseni
Kwaluseni is a town in Eswatini known primarily as the main campus site of the University of Eswatini.
-
C.
Lobamba
Lobamba is the traditional and legislative capital of Eswatini, serving as the seat of the Swazi monarchy and key national institutions.
-
D.
Nongoma
Nongoma is a town in KwaZulu-Natal, South Africa, historically significant as a center of Zulu royalty and traditional leadership.
-
E.
Lanseria
Lanseria is a town in the northwestern part of Johannesburg, South Africa, known primarily for hosting the privately owned Lanseria International Airport.
- 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_69c68839ccb88190b4aa5cc1aca3448f |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d9c00e948190b103a2b2a2738bb1 |
completed | March 27, 2026, 7:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c749076f6c819088b0b40dd3e208b0 |
completed | March 28, 2026, 3:20 a.m. |
Created at: March 27, 2026, 2:25 p.m.