Triple
T3146313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2019 WNBA season |
E65771
|
entity |
| Predicate | commissioner |
P3178
|
FINISHED |
| Object |
Cathy Engelbert
Cathy Engelbert is an American business executive who became the first commissioner of the WNBA after previously serving as CEO of Deloitte.
|
E432372
|
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: Cathy Engelbert | Statement: [2019 WNBA season, commissioner, Cathy Engelbert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cathy Engelbert Context triple: [2019 WNBA season, commissioner, Cathy Engelbert]
-
A.
Cynthia Ludwig
Cynthia Ludwig is a film editor known for her work on the 2009 horror film "My Bloody Valentine 3D."
-
B.
Cathy Konrad
Cathy Konrad is an American film and television producer known for her work on acclaimed projects such as "Walk the Line," "Girl, Interrupted," and the TV series "Scream."
-
C.
Kathy Speer
Kathy Speer is an American television writer and producer best known for her work on popular sitcoms such as The Golden Girls and its spin-off The Golden Palace.
-
D.
Catherine Stihler
Catherine Stihler is a Scottish former Labour MEP and political figure who has gone on to lead major digital and open knowledge organizations.
-
E.
Cathy Douglas
Cathy Douglas was the wife of longtime U.S. Supreme Court Justice William O. Douglas.
- 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: Cathy Engelbert Triple: [2019 WNBA season, commissioner, Cathy Engelbert]
Generated description
Cathy Engelbert is an American business executive who became the first commissioner of the WNBA after previously serving as CEO of Deloitte.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cathy Engelbert Target entity description: Cathy Engelbert is an American business executive who became the first commissioner of the WNBA after previously serving as CEO of Deloitte.
-
A.
Cynthia Ludwig
Cynthia Ludwig is a film editor known for her work on the 2009 horror film "My Bloody Valentine 3D."
-
B.
Cathy Konrad
Cathy Konrad is an American film and television producer known for her work on acclaimed projects such as "Walk the Line," "Girl, Interrupted," and the TV series "Scream."
-
C.
Kathy Speer
Kathy Speer is an American television writer and producer best known for her work on popular sitcoms such as The Golden Girls and its spin-off The Golden Palace.
-
D.
Catherine Stihler
Catherine Stihler is a Scottish former Labour MEP and political figure who has gone on to lead major digital and open knowledge organizations.
-
E.
Cathy Douglas
Cathy Douglas was the wife of longtime U.S. Supreme Court Justice William O. Douglas.
- 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_69ad8582f564819088c27e1f96153938 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada598ccf08190b8817c456f38f2d7 |
completed | March 8, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5db52bcfc8190857a3ea5157d8416 |
completed | March 14, 2026, 10:04 p.m. |
| NEDg | Description generation | batch_69b5dbe844e4819099dbd1ed65f262fb |
completed | March 14, 2026, 10:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5dc5a2b008190907150ada5714fac |
completed | March 14, 2026, 10:08 p.m. |
Created at: March 8, 2026, 3:05 p.m.