Triple
T21245394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahesana district |
E523593
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Kadi
Kadi is a town in the Mehsana district of Gujarat, India, known for its agricultural markets and growing industrial activities.
|
E1472936
|
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: Kadi | Statement: [Mahesana district, hasTown, Kadi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kadi Context triple: [Mahesana district, hasTown, Kadi]
-
A.
Grand Kadi
The Grand Kadi is the highest-ranking Islamic judicial authority in a state’s Sharia court system, overseeing the application and interpretation of Islamic law in appellate matters.
-
B.
Mawlawiyya
Mawlawiyya is a Sufi order best known for its whirling dervish ceremonies and spiritual practices inspired by the teachings of the Persian poet and mystic Rumi.
-
C.
Fardis
Fardis is a city in Iran that serves as an urban center within the country's Alborz Province.
-
D.
Hedaya
Hedaya is a surname most notably associated with American character actor Dan Hedaya, known for his numerous film and television roles.
-
E.
Muladis
Muladis were Muslims in medieval Iberia who were originally local Christians that had converted to Islam, often blending Arab-Islamic and Iberian cultural elements.
- 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: Kadi Triple: [Mahesana district, hasTown, Kadi]
Generated description
Kadi is a town in the Mehsana district of Gujarat, India, known for its agricultural markets and growing industrial activities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kadi Target entity description: Kadi is a town in the Mehsana district of Gujarat, India, known for its agricultural markets and growing industrial activities.
-
A.
Grand Kadi
The Grand Kadi is the highest-ranking Islamic judicial authority in a state’s Sharia court system, overseeing the application and interpretation of Islamic law in appellate matters.
-
B.
Mawlawiyya
Mawlawiyya is a Sufi order best known for its whirling dervish ceremonies and spiritual practices inspired by the teachings of the Persian poet and mystic Rumi.
-
C.
Fardis
Fardis is a city in Iran that serves as an urban center within the country's Alborz Province.
-
D.
Hedaya
Hedaya is a surname most notably associated with American character actor Dan Hedaya, known for his numerous film and television roles.
-
E.
Muladis
Muladis were Muslims in medieval Iberia who were originally local Christians that had converted to Islam, often blending Arab-Islamic and Iberian cultural elements.
- 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_69e0b513b89c81908b27147e91368db2 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73599e5548190ad70d2c2bfa9e919 |
completed | April 21, 2026, 8:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0986f705808190ba3a85d2dacb8818 |
completed | May 17, 2026, 9:14 a.m. |
| NEDg | Description generation | batch_6a0988086a648190856057c1327a6ab1 |
completed | May 17, 2026, 9:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0988d4ada081909fe532955b204b94 |
completed | May 17, 2026, 9:22 a.m. |
Created at: April 16, 2026, 3:47 p.m.