Triple
T4633133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nabeul Governorate |
E101463
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Dar Chaabane
Dar Chaabane is a coastal town in northeastern Tunisia known for its traditional architecture and proximity to the Mediterranean Sea.
|
E458669
|
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: Dar Chaabane | Statement: [Nabeul Governorate, hasCity, Dar Chaabane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dar Chaabane Context triple: [Nabeul Governorate, hasCity, Dar Chaabane]
-
A.
Sama Chakeva
Sama Chakeva is a traditional folk festival of the Mithila region celebrating the bond between brothers and sisters through songs, rituals, and decorative clay idols of birds.
-
B.
Zabana
Zabana is an Oceanic language spoken in the Solomon Islands, primarily on Santa Isabel Island.
-
C.
El Omrane
El Omrane is a district of Tunis, Tunisia, known as a largely residential urban area within the capital’s metropolitan region.
-
D.
El Tebbin
El Tebbin is an industrial district in southern Cairo, Egypt, known for its steel and heavy manufacturing facilities.
-
E.
Azéma
Azéma is a French surname most notably borne by architect Léon Azéma, known for his contributions to early 20th-century French public architecture.
- 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: Dar Chaabane Triple: [Nabeul Governorate, hasCity, Dar Chaabane]
Generated description
Dar Chaabane is a coastal town in northeastern Tunisia known for its traditional architecture and proximity to the Mediterranean Sea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dar Chaabane Target entity description: Dar Chaabane is a coastal town in northeastern Tunisia known for its traditional architecture and proximity to the Mediterranean Sea.
-
A.
Sama Chakeva
Sama Chakeva is a traditional folk festival of the Mithila region celebrating the bond between brothers and sisters through songs, rituals, and decorative clay idols of birds.
-
B.
Zabana
Zabana is an Oceanic language spoken in the Solomon Islands, primarily on Santa Isabel Island.
-
C.
El Omrane
El Omrane is a district of Tunis, Tunisia, known as a largely residential urban area within the capital’s metropolitan region.
-
D.
El Tebbin
El Tebbin is an industrial district in southern Cairo, Egypt, known for its steel and heavy manufacturing facilities.
-
E.
Azéma
Azéma is a French surname most notably borne by architect Léon Azéma, known for his contributions to early 20th-century French public architecture.
- 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_69bd43d2f1c081908cd4b7ec48ecc73d |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a5d0de881909baacc5b991f5b53 |
completed | March 20, 2026, 2:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfac317248190a8886d59d2242acb |
completed | March 21, 2026, 1:56 a.m. |
| NEDg | Description generation | batch_69bdfceda19c8190909c21594ea792a0 |
completed | March 21, 2026, 2:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdfd7790748190b322d7f8109fa887 |
completed | March 21, 2026, 2:07 a.m. |
Created at: March 20, 2026, 1:13 p.m.