Triple
T14830204
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Askari Mirza |
E348678
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Askari
Askari is a masculine given name of Persian and Arabic origin, historically borne by several notable figures in South and Central Asia.
|
E1122267
|
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: Askari | Statement: [Askari Mirza, givenName, Askari]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Askari Context triple: [Askari Mirza, givenName, Askari]
-
A.
Afghan Commandos
Afghan Commandos were elite special operations forces of Afghanistan, known for conducting high-risk counterinsurgency and counterterrorism missions alongside coalition troops.
-
B.
Zaka
Zaka is a rural district and administrative center located in southeastern Zimbabwe.
-
C.
Fauji
Fauji is an Indian television miniseries that marked Shah Rukh Khan’s breakthrough role, depicting the training and lives of army commandos.
-
D.
Warhad
Warhad is an old regional name historically used for the area later known as Berar in central India.
-
E.
Kabuliwala
Kabuliwala is a celebrated short story by Rabindranath Tagore that portrays the poignant bond between an Afghan fruit seller and a young Bengali girl, exploring themes of fatherhood, separation, and human connection.
- 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: Askari Triple: [Askari Mirza, givenName, Askari]
Generated description
Askari is a masculine given name of Persian and Arabic origin, historically borne by several notable figures in South and Central Asia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Askari Target entity description: Askari is a masculine given name of Persian and Arabic origin, historically borne by several notable figures in South and Central Asia.
-
A.
Afghan Commandos
Afghan Commandos were elite special operations forces of Afghanistan, known for conducting high-risk counterinsurgency and counterterrorism missions alongside coalition troops.
-
B.
Zaka
Zaka is a rural district and administrative center located in southeastern Zimbabwe.
-
C.
Fauji
Fauji is an Indian television miniseries that marked Shah Rukh Khan’s breakthrough role, depicting the training and lives of army commandos.
-
D.
Warhad
Warhad is an old regional name historically used for the area later known as Berar in central India.
-
E.
Kabuliwala
Kabuliwala is a celebrated short story by Rabindranath Tagore that portrays the poignant bond between an Afghan fruit seller and a young Bengali girl, exploring themes of fatherhood, separation, and human connection.
- 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded0748eec8190a39c94024c3a3e3e |
completed | April 14, 2026, 11:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe38a16fd881909d246d8d1811a673 |
completed | May 8, 2026, 7:25 p.m. |
| NEDg | Description generation | batch_69fe502938dc8190892373403077cfdd |
completed | May 8, 2026, 9:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe509a02c08190bd2171b16584cd64 |
completed | May 8, 2026, 9:07 p.m. |
Created at: April 10, 2026, 1:51 a.m.