Triple
T7663027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wits Art Museum |
E173553
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
WAM
WAM is a university art museum in Johannesburg, South Africa, known for its extensive collection of African art and its role in research and education at the University of the Witwatersrand.
|
E680066
|
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: WAM | Statement: [Wits Art Museum, shortName, WAM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WAM Context triple: [Wits Art Museum, shortName, WAM]
-
A.
WAMO
WAMO is a Pittsburgh-area radio station historically known for its urban contemporary and hip-hop programming serving the region’s Black community.
-
B.
WM
WM was the reporting mark for the Western Maryland Railway, a regional U.S. railroad that later became part of the Chessie System.
-
C.
WAW
WAW is the three-letter IATA airport code for Warsaw Chopin Airport, the primary international airport serving Warsaw, Poland.
-
D.
WEM
WEM is a massive shopping and entertainment complex in Edmonton, Alberta, known as one of the largest malls in North America.
-
E.
WMC
WMC is the IATA airport code for Winnemucca Municipal Airport, a public airport serving the Winnemucca area in Nevada, United States.
- 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: WAM Triple: [Wits Art Museum, shortName, WAM]
Generated description
WAM is a university art museum in Johannesburg, South Africa, known for its extensive collection of African art and its role in research and education at the University of the Witwatersrand.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: WAM Target entity description: WAM is a university art museum in Johannesburg, South Africa, known for its extensive collection of African art and its role in research and education at the University of the Witwatersrand.
-
A.
WAMO
WAMO is a Pittsburgh-area radio station historically known for its urban contemporary and hip-hop programming serving the region’s Black community.
-
B.
WM
WM was the reporting mark for the Western Maryland Railway, a regional U.S. railroad that later became part of the Chessie System.
-
C.
WAW
WAW is the three-letter IATA airport code for Warsaw Chopin Airport, the primary international airport serving Warsaw, Poland.
-
D.
WEM
WEM is a massive shopping and entertainment complex in Edmonton, Alberta, known as one of the largest malls in North America.
-
E.
WMC
WMC is the IATA airport code for Winnemucca Municipal Airport, a public airport serving the Winnemucca area in Nevada, United States.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701a74a2c81909f78ab2de7ce807c |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c89b1aaef081908d1d181ea7c28c2f |
completed | March 29, 2026, 3:23 a.m. |
| NEDg | Description generation | batch_69c89e177fd08190a6f3a70cf32365d9 |
completed | March 29, 2026, 3:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c89e7328f4819088651b60e7af457d |
completed | March 29, 2026, 3:37 a.m. |
Created at: March 27, 2026, 3:59 p.m.