Triple
T14062917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Data Darbar |
E338390
|
entity |
| Predicate | alternativeName |
P39
|
FINISHED |
| Object |
Data Sahib
Data Sahib is a revered Sufi saint associated with the famous Data Darbar shrine in Lahore, Pakistan.
|
E1077620
|
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: Data Sahib | Statement: [Data Darbar, alternativeName, Data Sahib]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Data Sahib Context triple: [Data Darbar, alternativeName, Data Sahib]
-
A.
Dati
Dati is a surname most notably associated with Rachida Dati, a prominent French politician and former Minister of Justice.
-
B.
Datu
Datu is a traditional title for a chieftain or local ruler in pre-colonial Philippine societies.
-
C.
Data61
Data61 is an Australian national data science and digital innovation research organization within CSIRO, focused on advanced analytics, cybersecurity, and emerging technologies.
-
D.
Data's Day
"Data's Day" is a character-focused episode of Star Trek: The Next Generation that follows the android officer Data as he narrates a typical day aboard the Enterprise, exploring human relationships and emotions from his unique perspective.
-
E.
Data
Data is an android Starfleet officer in Star Trek: The Next Generation, known for his quest to understand humanity and develop emotions.
- 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: Data Sahib Triple: [Data Darbar, alternativeName, Data Sahib]
Generated description
Data Sahib is a revered Sufi saint associated with the famous Data Darbar shrine in Lahore, Pakistan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Data Sahib Target entity description: Data Sahib is a revered Sufi saint associated with the famous Data Darbar shrine in Lahore, Pakistan.
-
A.
Dati
Dati is a surname most notably associated with Rachida Dati, a prominent French politician and former Minister of Justice.
-
B.
Datu
Datu is a traditional title for a chieftain or local ruler in pre-colonial Philippine societies.
-
C.
Data61
Data61 is an Australian national data science and digital innovation research organization within CSIRO, focused on advanced analytics, cybersecurity, and emerging technologies.
-
D.
Data's Day
"Data's Day" is a character-focused episode of Star Trek: The Next Generation that follows the android officer Data as he narrates a typical day aboard the Enterprise, exploring human relationships and emotions from his unique perspective.
-
E.
Data
Data is an android Starfleet officer in Star Trek: The Next Generation, known for his quest to understand humanity and develop emotions.
- 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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de568876308190840361dcaf10bd45 |
completed | April 14, 2026, 3 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcb6673de881909653c7691422b800 |
completed | May 7, 2026, 3:57 p.m. |
| NEDg | Description generation | batch_69fcc3b8aea881909b11a4671314810e |
completed | May 7, 2026, 4:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fcc450fea881909e3b5bbd96469a8b |
completed | May 7, 2026, 4:56 p.m. |
Created at: April 9, 2026, 10:21 p.m.