Triple
T2499180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ASFAR Rabat |
E52421
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
ASFAR
ASFAR is a prominent Moroccan football club based in Rabat, known for its strong domestic record and association with the Royal Moroccan Armed Forces.
|
E273293
|
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: ASFAR | Statement: [ASFAR Rabat, shortName, ASFAR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ASFAR Context triple: [ASFAR Rabat, shortName, ASFAR]
-
A.
AFRALO
AFRALO is the African Regional At-Large Organization within ICANN, representing the interests of individual Internet users across the African region in global Internet governance.
-
B.
Afif
Afif is a town in central Saudi Arabia known as an inland community within the Riyadh administrative region.
-
C.
Nasar
Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
-
D.
Faras
Faras was an important ancient city in Nubia, known especially for its Christian-era cathedral and remarkable wall paintings discovered during archaeological excavations.
-
E.
Baashha
Baashha is a hugely popular 1995 Tamil action film starring Rajinikanth, celebrated for its iconic dialogues, mass appeal, and enduring cult status in Indian cinema.
- 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: ASFAR Triple: [ASFAR Rabat, shortName, ASFAR]
Generated description
ASFAR is a prominent Moroccan football club based in Rabat, known for its strong domestic record and association with the Royal Moroccan Armed Forces.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ASFAR Target entity description: ASFAR is a prominent Moroccan football club based in Rabat, known for its strong domestic record and association with the Royal Moroccan Armed Forces.
-
A.
AFRALO
AFRALO is the African Regional At-Large Organization within ICANN, representing the interests of individual Internet users across the African region in global Internet governance.
-
B.
Afif
Afif is a town in central Saudi Arabia known as an inland community within the Riyadh administrative region.
-
C.
Nasar
Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
-
D.
Faras
Faras was an important ancient city in Nubia, known especially for its Christian-era cathedral and remarkable wall paintings discovered during archaeological excavations.
-
E.
Baashha
Baashha is a hugely popular 1995 Tamil action film starring Rajinikanth, celebrated for its iconic dialogues, mass appeal, and enduring cult status in Indian cinema.
- 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1afd86c81909181c4b45d4f8bc5 |
completed | March 7, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1f9ed13c81909856db636bfb2e9e |
completed | March 9, 2026, 7:29 p.m. |
| NEDg | Description generation | batch_69af23a305a48190b457b1b66779b90d |
completed | March 9, 2026, 7:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af240855848190947190a662745b77 |
completed | March 9, 2026, 7:48 p.m. |
Created at: March 6, 2026, 9:46 p.m.