Triple
T3559629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inter Milan |
E75303
|
entity |
| Predicate | chairman |
P377
|
FINISHED |
| Object |
Steven Zhang
Steven Zhang is a Chinese businessman best known for serving as the president of Italian football club Inter Milan.
|
E370545
|
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: Steven Zhang | Statement: [Inter Milan, chairman, Steven Zhang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steven Zhang Context triple: [Inter Milan, chairman, Steven Zhang]
-
A.
Daniel Zhang
Daniel Zhang is a Chinese business executive best known for leading Alibaba Group through a major period of global expansion and for creating the Singles’ Day shopping festival.
-
B.
James Zhou
James Zhou is a Chinese businessman best known as the owner and chairman of French football club AJ Auxerre.
-
C.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
-
D.
Xiangyu Zhang
Xiangyu Zhang is a computer vision and deep learning researcher known for his contributions to convolutional neural network architectures and large-scale visual recognition.
-
E.
Christopher Chung
Christopher Chung is an actor known for his role in the British spy drama series "Slow Horses."
- 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: Steven Zhang Triple: [Inter Milan, chairman, Steven Zhang]
Generated description
Steven Zhang is a Chinese businessman best known for serving as the president of Italian football club Inter Milan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Steven Zhang Target entity description: Steven Zhang is a Chinese businessman best known for serving as the president of Italian football club Inter Milan.
-
A.
Daniel Zhang
Daniel Zhang is a Chinese business executive best known for leading Alibaba Group through a major period of global expansion and for creating the Singles’ Day shopping festival.
-
B.
James Zhou
James Zhou is a Chinese businessman best known as the owner and chairman of French football club AJ Auxerre.
-
C.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
-
D.
Xiangyu Zhang
Xiangyu Zhang is a computer vision and deep learning researcher known for his contributions to convolutional neural network architectures and large-scale visual recognition.
-
E.
Christopher Chung
Christopher Chung is an actor known for his role in the British spy drama series "Slow Horses."
- 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_69ad85d45090819086f34fb85d850a1e |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc089270c81908bc200c84fe1592e |
completed | March 8, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb9de4bc8190ba5d111465e66cf8 |
completed | March 13, 2026, 7:24 a.m. |
| NEDg | Description generation | batch_69b3bf85f4f881908bfd2dcfaa3537af |
completed | March 13, 2026, 7:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3fb35c3a08190b50c87923037ee07 |
completed | March 13, 2026, 11:55 a.m. |
Created at: March 8, 2026, 3:20 p.m.