Triple
T11452067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | El Tebbin |
E271421
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Maasara
Maasara is an industrial and residential district in the Greater Cairo area of Egypt, known for its factories and proximity to the Nile.
|
E928328
|
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: Maasara | Statement: [El Tebbin, locatedNear, Maasara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maasara Context triple: [El Tebbin, locatedNear, Maasara]
-
A.
Maasi
Maasi is a late-winter month in the traditional Tamil calendar, typically corresponding to February–March in the Gregorian calendar and associated with various Hindu religious observances.
-
B.
Majene
Majene is a coastal town and regency capital in West Sulawesi, Indonesia, known for its fishing industry and role as a regional administrative center.
-
C.
Mambasa
Mambasa is a town and administrative center located in the forested Ituri region of northeastern Democratic Republic of the Congo.
-
D.
Masar
Masar is a British Thoroughbred racehorse best known for winning the 2018 Epsom Derby for Godolphin.
-
E.
Mamfe
Mamfe is a town in western Cameroon known as an important local trade and transport hub near the Nigerian border.
- 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: Maasara Triple: [El Tebbin, locatedNear, Maasara]
Generated description
Maasara is an industrial and residential district in the Greater Cairo area of Egypt, known for its factories and proximity to the Nile.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maasara Target entity description: Maasara is an industrial and residential district in the Greater Cairo area of Egypt, known for its factories and proximity to the Nile.
-
A.
Maasi
Maasi is a late-winter month in the traditional Tamil calendar, typically corresponding to February–March in the Gregorian calendar and associated with various Hindu religious observances.
-
B.
Majene
Majene is a coastal town and regency capital in West Sulawesi, Indonesia, known for its fishing industry and role as a regional administrative center.
-
C.
Mambasa
Mambasa is a town and administrative center located in the forested Ituri region of northeastern Democratic Republic of the Congo.
-
D.
Masar
Masar is a British Thoroughbred racehorse best known for winning the 2018 Epsom Derby for Godolphin.
-
E.
Mamfe
Mamfe is a town in western Cameroon known as an important local trade and transport hub near the Nigerian border.
- 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d81c6f4d788190ac59b0df946cebbc |
completed | April 9, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6040733648190a10f9553b3ac87a7 |
completed | April 20, 2026, 10:46 a.m. |
| NEDg | Description generation | batch_69e610a07bf881908de79850edb9576f |
completed | April 20, 2026, 11:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e617fdaaa88190a1860fb00309596b |
completed | April 20, 2026, 12:11 p.m. |
Created at: April 8, 2026, 9:35 p.m.