Triple
T16044249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gash-Barka Region |
E389174
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Logo Anseba
Logo Anseba is a town and administrative settlement located in Eritrea’s Gash-Barka region.
|
E1191809
|
NE FINISHED |
How this triple was built (4 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: Logo Anseba | Statement: [Gash-Barka Region, hasSettlement, Logo Anseba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Logo Anseba Context triple: [Gash-Barka Region, hasSettlement, Logo Anseba]
-
A.
Logos
Logos is a central concept in Christian theology referring to the divine Word or reason of God, identified with Christ as the preexistent and incarnate Son.
-
B.
Ebasco
Ebasco was a major American engineering and construction firm known for designing and building large-scale power plants, including nuclear facilities worldwide.
-
C.
Logo
Logo is an educational programming language known for its turtle graphics, designed to help learners explore mathematical and computational ideas through simple commands.
-
D.
Logo
Logo is an American cable television channel that primarily targets LGBTQ+ audiences with a mix of original series, films, and reality programming.
-
E.
The Logo
The Logo is the famous nickname of NBA legend Jerry West, referencing his iconic silhouette used in the league’s official logo.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Logo Anseba Triple: [Gash-Barka Region, hasSettlement, Logo Anseba]
Generated description
Logo Anseba is a town and administrative settlement located in Eritrea’s Gash-Barka region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Logo Anseba Target entity description: Logo Anseba is a town and administrative settlement located in Eritrea’s Gash-Barka region.
-
A.
Logos
Logos is a central concept in Christian theology referring to the divine Word or reason of God, identified with Christ as the preexistent and incarnate Son.
-
B.
Ebasco
Ebasco was a major American engineering and construction firm known for designing and building large-scale power plants, including nuclear facilities worldwide.
-
C.
Logo
Logo is an educational programming language known for its turtle graphics, designed to help learners explore mathematical and computational ideas through simple commands.
-
D.
Logo
Logo is an American cable television channel that primarily targets LGBTQ+ audiences with a mix of original series, films, and reality programming.
-
E.
The Logo
The Logo is the famous nickname of NBA legend Jerry West, referencing his iconic silhouette used in the league’s official logo.
- F. None of above. chosen
Provenance (5 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. |
| NEDg | Description generation | batch_69ffde10adec81908c0b662780184131 |
completed | May 10, 2026, 1:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffde9037848190b8d3b84fdec93ed6 |
completed | May 10, 2026, 1:25 a.m. |
Created at: April 10, 2026, 4:56 a.m.