Triple
T4320750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bourse de Tunis |
E96508
|
entity |
| Predicate | hasIndex |
P2915
|
FINISHED |
| Object |
Tunindex 20
Tunindex 20 is a benchmark stock market index comprising the 20 most actively traded and capitalized companies listed on the Tunis Stock Exchange.
|
E432205
|
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: Tunindex 20 | Statement: [Bourse de Tunis, hasIndex, Tunindex 20]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tunindex 20 Context triple: [Bourse de Tunis, hasIndex, Tunindex 20]
-
A.
TUNAIR
TUNAIR is the radio callsign used by Tunisair, the national flag carrier airline of Tunisia.
-
B.
Tunechi
Tunechi is a popular nickname and alter ego of American rapper Lil Wayne, often used to refer to his distinctive persona and musical brand.
-
C.
In Tune
In Tune is a long-running BBC Radio 3 magazine programme featuring live classical music performances, interviews with musicians, and arts news.
-
D.
T2
T2 is San Francisco International Airport’s Terminal 2, a modern passenger terminal serving domestic flights with updated amenities and design.
-
E.
T2
T2 is a passenger terminal at Berlin Brandenburg Airport that handles check-in, security, and boarding operations for departing and arriving travelers.
- 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: Tunindex 20 Triple: [Bourse de Tunis, hasIndex, Tunindex 20]
Generated description
Tunindex 20 is a benchmark stock market index comprising the 20 most actively traded and capitalized companies listed on the Tunis Stock Exchange.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tunindex 20 Target entity description: Tunindex 20 is a benchmark stock market index comprising the 20 most actively traded and capitalized companies listed on the Tunis Stock Exchange.
-
A.
TUNAIR
TUNAIR is the radio callsign used by Tunisair, the national flag carrier airline of Tunisia.
-
B.
Tunechi
Tunechi is a popular nickname and alter ego of American rapper Lil Wayne, often used to refer to his distinctive persona and musical brand.
-
C.
In Tune
In Tune is a long-running BBC Radio 3 magazine programme featuring live classical music performances, interviews with musicians, and arts news.
-
D.
T2
T2 is a shorthand title for the 1991 science fiction action film "Terminator 2: Judgment Day," directed by James Cameron and starring Arnold Schwarzenegger.
-
E.
T2
T2 is San Francisco International Airport’s Terminal 2, a modern passenger terminal serving domestic flights with updated amenities and design.
- 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_69b345422aac81909ddbadae437d122e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35114ed2c8190949c5d8032d7b921 |
completed | March 12, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d08d70408190aca5793a480cf54f |
completed | March 14, 2026, 9:18 p.m. |
| NEDg | Description generation | batch_69b5d4607a688190a3a7352579ea5ee7 |
completed | March 14, 2026, 9:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5d51068dc819099ac28361188dcc4 |
completed | March 14, 2026, 9:37 p.m. |
Created at: March 12, 2026, 11:12 p.m.