Triple
T2648083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terence Tao |
E53830
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Terence Chi-Shen Tao |
E53830
|
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: Terence Chi-Shen Tao | Statement: [Terence Tao, birthName, Terence Chi-Shen Tao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terence Chi-Shen Tao Context triple: [Terence Tao, birthName, Terence Chi-Shen Tao]
-
A.
Terence Tao
chosen
Terence Tao is an Australian-American mathematician renowned for his groundbreaking work in harmonic analysis, partial differential equations, additive combinatorics, and analytic number theory.
-
B.
Yee-Whye Teh
Yee-Whye Teh is a prominent statistician and machine learning researcher known for his influential work on Bayesian nonparametrics, probabilistic modeling, and deep learning.
-
C.
Langche Zeng
Langche Zeng is a political scientist and quantitative methodologist known for his collaborative work with Gary King on statistical methods in social science research.
-
D.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
-
E.
Mung Chiang
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.
- 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_69ab495e192081909c77b622e8e7e15a |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd91b4b3c81908571e85a1621dfc5 |
completed | March 7, 2026, 7:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa052c91c8190abfd49dbc62a4448 |
completed | March 10, 2026, 4:38 a.m. |
Created at: March 6, 2026, 9:53 p.m.