Triple
T15477073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gilan Province |
E376809
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Lahijan |
E713581
|
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: Lahijan | Statement: [Gilan Province, hasMajorCity, Lahijan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lahijan Context triple: [Gilan Province, hasMajorCity, Lahijan]
-
A.
Lahijan
chosen
Lahijan is a prominent city in Iran’s Gilan Province, known for its lush tea plantations, scenic Caspian Sea–adjacent landscapes, and role as a regional cultural and economic center.
-
B.
Lahij
Lahij is a historic town in southwestern Yemen known for its role as an administrative and cultural center in the region.
-
C.
Harjola
Harjola is a Finnish surname, notably borne by film director Renny Harlin (born Lauri Mauritz Harjola).
-
D.
Liausson
Liausson is a small commune in southern France’s Hérault department, known for its scenic setting on the shores of the artificial Lac du Salagou.
-
E.
Kihlaus
Kihlaus is a comedic play by Finnish author Aleksis Kivi, known for its humorous portrayal of rural life and courtship.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f88a5dc8190a2d7830748e29180 |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2d0b3e7881908f195701fe222371 |
completed | May 9, 2026, 12:48 p.m. |
Created at: April 10, 2026, 3:34 a.m.