Triple
T12741850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ludhiana district |
E304506
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Samrala
Samrala is a town in the Indian state of Punjab, known for its agricultural surroundings and location within the Ludhiana region.
|
E1010346
|
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: Samrala | Statement: [Ludhiana district, containsTown, Samrala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samrala Context triple: [Ludhiana district, containsTown, Samrala]
-
A.
Arjan Garh
Arjan Garh is an elevated station on the Delhi Metro network serving the southern outskirts of Delhi near the Haryana border.
-
B.
Kishangarh
Kishangarh is a town and legislative assembly constituency in Rajasthan, India, known for its marble industry and distinctive miniature paintings.
-
C.
Kapadvanj
Kapadvanj is a historic town in the Kheda district of Gujarat, India, known for its traditional markets and regional cultural heritage.
-
D.
Sujangarh
Sujangarh is a town in the Indian state of Rajasthan known for its local markets, temples, and role as a regional commercial center.
-
E.
Naraingarh
Naraingarh is a town in the northern Indian state of Haryana, known for its agricultural surroundings and role as a local commercial center.
- 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: Samrala Triple: [Ludhiana district, containsTown, Samrala]
Generated description
Samrala is a town in the Indian state of Punjab, known for its agricultural surroundings and location within the Ludhiana region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Samrala Target entity description: Samrala is a town in the Indian state of Punjab, known for its agricultural surroundings and location within the Ludhiana region.
-
A.
Arjan Garh
Arjan Garh is an elevated station on the Delhi Metro network serving the southern outskirts of Delhi near the Haryana border.
-
B.
Kishangarh
Kishangarh is a town and legislative assembly constituency in Rajasthan, India, known for its marble industry and distinctive miniature paintings.
-
C.
Kapadvanj
Kapadvanj is a historic town in the Kheda district of Gujarat, India, known for its traditional markets and regional cultural heritage.
-
D.
Sujangarh
Sujangarh is a town in the Indian state of Rajasthan known for its local markets, temples, and role as a regional commercial center.
-
E.
Naraingarh
Naraingarh is a town in the northern Indian state of Haryana, known for its agricultural surroundings and role as a local commercial center.
- 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_69d7bdf1426c8190a4402e1c4cdec33a |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96bd321bc81908eb61cc05b550754 |
completed | April 10, 2026, 9:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af49d2c4819097168712af7d4c15 |
completed | May 3, 2026, 2:13 a.m. |
| NEDg | Description generation | batch_69f6b02e3b9881909387c1f70176a1bd |
completed | May 3, 2026, 2:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6b11ced30819090f67a0b1e1369aa |
completed | May 3, 2026, 2:21 a.m. |
Created at: April 9, 2026, 5:26 p.m.