Triple
T16044246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gash-Barka Region |
E389174
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Haykota |
E429519
|
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: Haykota | Statement: [Gash-Barka Region, hasSettlement, Haykota]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haykota Context triple: [Gash-Barka Region, hasSettlement, Haykota]
-
A.
Haykota
chosen
Haykota is a town in Eritrea’s Gash-Barka region, known primarily as a local agricultural and administrative center.
-
B.
Harahan
Harahan is a small suburban city in the Greater New Orleans area of Louisiana, known for its residential character and proximity to the Mississippi River.
-
C.
Halga
Halga is a legendary Danish prince from the Old English epic Beowulf, known as one of the Scylding royal brothers.
-
D.
Hagonoy
Hagonoy is a coastal municipality in the province of Bulacan in the Philippines, known for its fishing industry and aquaculture.
-
E.
Hagonoy
Hagonoy is a coastal agricultural municipality in the province of Davao del Sur in the Philippines.
- 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1835d1dac819089abec9f0668ec78 |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbd95d508190a21db435fb69f8d7 |
completed | May 10, 2026, 1:14 a.m. |
Created at: April 10, 2026, 4:56 a.m.