Triple
T22447073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BuckleScript |
E554887
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Hongbo Zhang |
—
|
NE NERFINISHED |
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: Hongbo Zhang | Statement: [BuckleScript, creator, Hongbo Zhang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hongbo Zhang Context triple: [BuckleScript, creator, Hongbo Zhang]
-
A.
Hongbo Zhang
chosen
Hongbo Zhang is a software engineer best known for creating BuckleScript, a compiler that translates OCaml/ReasonML code to efficient JavaScript.
-
B.
Hong-Kun Zhang
Hong-Kun Zhang is a mathematician known for her work in dynamical systems and ergodic theory, and for being a doctoral student of Lai-Sang Young.
-
C.
Tinghui Zhou
Tinghui Zhou is a computer vision and machine learning researcher known for influential work on unsupervised learning and image-to-image translation.
-
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.
Tingye Li
Tingye Li was a pioneering Chinese-American optical engineer and physicist renowned for his foundational contributions to laser and fiber-optic communications.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e5113208190ab58c6b595f9d1d0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b48be0481909f4601b732424e5b |
completed | April 29, 2026, 1:13 a.m. |
Created at: April 16, 2026, 8:47 p.m.