Triple
T7455017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mung Chiang |
E172100
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mung Chiang |
E172100
|
NE FINISHED |
How this triple was built (2 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: Mung Chiang | Statement: [Mung Chiang, name, Mung Chiang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mung Chiang Context triple: [Mung Chiang, name, Mung Chiang]
-
A.
Mung Chiang
chosen
Mung Chiang is an engineer and academic leader known for his work in electrical and computer engineering and for serving as president of Purdue University.
-
B.
Kuo-Chen Huang
Kuo-Chen Huang was a physicist whose work on electron–phonon coupling in solids led to the formulation of the Huang–Rhys factor in solid-state spectroscopy.
-
C.
Yu-Chi Ho
Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
-
D.
Tung-Mow Yan
Tung-Mow Yan is a theoretical physicist best known for co-formulating the Drell–Yan process, a fundamental mechanism for lepton pair production in high-energy particle collisions.
-
E.
Philip S. Yu
Philip S. Yu is a prominent computer scientist known for his influential contributions to data mining, databases, and big data analytics.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c68a66554c8190add75c65942c0317 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f3addd648190b618bfbffe08db2c |
completed | March 27, 2026, 9:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c827bedc408190a9a77f293fb12762 |
completed | March 28, 2026, 7:10 p.m. |
Created at: March 27, 2026, 3:15 p.m.