Triple
T14612980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akkar Governorate |
E343005
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Halba |
E1111260
|
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: Halba | Statement: [Akkar Governorate, hasCity, Halba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Halba Context triple: [Akkar Governorate, hasCity, Halba]
-
A.
Halba
chosen
Halba is a town in northern Lebanon that serves as the administrative and commercial center of the Akkar region.
-
B.
Halba
Halba are an indigenous Adivasi community of central India, particularly associated with the Bastar region, known for their distinct language, cultural traditions, and agrarian lifestyle.
-
C.
Hadern
Hadern is a borough in the southwest of Munich, Germany, known for its residential character and the large Waldfriedhof cemetery.
-
D.
Baar-Ebenhausen
Baar-Ebenhausen is a Bavarian municipality in southern Germany known for its residential character and location along the Ilm River.
-
E.
Hassela
Hassela is a small rural locality in northern Sweden known for its forested landscape and nearby ski and outdoor recreation areas.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb450e6588190a94488d8e71888c8 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde16c005c81908b54fcfd4243d820 |
completed | May 8, 2026, 1:13 p.m. |
Created at: April 10, 2026, 1:25 a.m.