Triple
T15872578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Louis School |
E384865
|
entity |
| Predicate | producedAthlete |
P17934
|
FINISHED |
| Object |
Timmy Chang
Timmy Chang is a former record-setting University of Hawaiʻi quarterback and current college football coach.
|
E1181351
|
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: Timmy Chang | Statement: [Saint Louis School, producedAthlete, Timmy Chang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Timmy Chang Context triple: [Saint Louis School, producedAthlete, Timmy Chang]
-
A.
Ben Chang
Ben Chang is a chaotic and eccentric Spanish teacher-turned-student from the TV sitcom "Community," known for his unpredictable behavior and over-the-top antics.
-
B.
Timmy Hung
Timmy Hung is a Hong Kong actor and television personality known for his work in film and TV as well as being the son of martial arts star Sammo Hung.
-
C.
Steve Chang
Steve Chang is a Taiwanese entrepreneur best known for co-founding and leading the cybersecurity company Trend Micro.
-
D.
Christopher Chung
Christopher Chung is an actor known for his role in the British spy drama series "Slow Horses."
-
E.
Felix Chong
Felix Chong is a Hong Kong filmmaker best known as the co-writer and co-creator of the acclaimed crime thriller series "Infernal Affairs," which inspired Martin Scorsese’s "The Departed."
- 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: Timmy Chang Triple: [Saint Louis School, producedAthlete, Timmy Chang]
Generated description
Timmy Chang is a former record-setting University of Hawaiʻi quarterback and current college football coach.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Timmy Chang Target entity description: Timmy Chang is a former record-setting University of Hawaiʻi quarterback and current college football coach.
-
A.
Ben Chang
Ben Chang is a chaotic and eccentric Spanish teacher-turned-student from the TV sitcom "Community," known for his unpredictable behavior and over-the-top antics.
-
B.
Timmy Hung
Timmy Hung is a Hong Kong actor and television personality known for his work in film and TV as well as being the son of martial arts star Sammo Hung.
-
C.
Steve Chang
Steve Chang is a Taiwanese entrepreneur best known for co-founding and leading the cybersecurity company Trend Micro.
-
D.
Christopher Chung
Christopher Chung is an actor known for his role in the British spy drama series "Slow Horses."
-
E.
Felix Chong
Felix Chong is a Hong Kong filmmaker best known as the co-writer and co-creator of the acclaimed crime thriller series "Infernal Affairs," which inspired Martin Scorsese’s "The Departed."
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e155faefd08190af634867370796b3 |
completed | April 16, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa94ca15c8190bdd5fe0a30b54b51 |
completed | May 9, 2026, 9:38 p.m. |
| NEDg | Description generation | batch_69ffaa3903408190b7beaa6b461bd2bd |
completed | May 9, 2026, 9:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffab0c79d4819085f0ed6a4edcb7fb |
completed | May 9, 2026, 9:45 p.m. |
Created at: April 10, 2026, 4:51 a.m.