Triple
T18748184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wansjaliya Junction |
E458456
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
WSJ
WSJ is the Indian Railways station code for Wansjaliya Junction railway station in Gujarat, India.
|
E1342186
|
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: WSJ | Statement: [Wansjaliya Junction, hasStationCode, WSJ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WSJ Context triple: [Wansjaliya Junction, hasStationCode, WSJ]
-
A.
The Wall Street Journal
The Wall Street Journal is a leading American business-focused daily newspaper known for its influential financial reporting and analysis.
-
B.
BusinessWeek
BusinessWeek is a major American business magazine known for its coverage of global markets, companies, and economic trends.
-
C.
Bloomberg News
Bloomberg News is a global financial and business news organization known for its real-time market coverage, data-driven reporting, and multimedia journalism.
-
D.
The Economist
The Economist is an international weekly news and business publication known for its in-depth analysis and commentary on global politics, economics, and current affairs.
-
E.
Financial Times
The Financial Times is a leading international daily newspaper based in London, renowned for its global business, economic, and financial news coverage.
- 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: WSJ Triple: [Wansjaliya Junction, hasStationCode, WSJ]
Generated description
WSJ is the Indian Railways station code for Wansjaliya Junction railway station in Gujarat, India.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: WSJ Target entity description: WSJ is the Indian Railways station code for Wansjaliya Junction railway station in Gujarat, India.
-
A.
The Wall Street Journal
The Wall Street Journal is a leading American business-focused daily newspaper known for its influential financial reporting and analysis.
-
B.
BusinessWeek
BusinessWeek is a major American business magazine known for its coverage of global markets, companies, and economic trends.
-
C.
Bloomberg News
Bloomberg News is a global financial and business news organization known for its real-time market coverage, data-driven reporting, and multimedia journalism.
-
D.
The Economist
The Economist is an international weekly news and business publication known for its in-depth analysis and commentary on global politics, economics, and current affairs.
-
E.
Financial Times
The Financial Times is a leading international daily newspaper based in London, renowned for its global business, economic, and financial news coverage.
- 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_69d8d394dc308190b6725073f5db324c |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e579e9903c81908180c46012bd0948 |
completed | April 20, 2026, 12:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a053d350fbc81909969b6fa502af370 |
completed | May 14, 2026, 3:10 a.m. |
| NEDg | Description generation | batch_6a05414a5b6c81908da562c6322e000d |
completed | May 14, 2026, 3:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a054217f8cc8190810d9f99ee6a5f9c |
completed | May 14, 2026, 3:31 a.m. |
Created at: April 10, 2026, 11:51 a.m.