Triple
T7984349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Shang |
E185652
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Lord Shang |
E185652
|
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: Lord Shang | Statement: [Lord Shang, name, Lord Shang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lord Shang Context triple: [Lord Shang, name, Lord Shang]
-
A.
Lord Shang
chosen
Lord Shang was an influential Chinese statesman and legalist reformer of the Warring States period, best known for transforming the state of Qin into a highly centralized and powerful military state.
-
B.
Lord Shen
Lord Shen is the main peacock antagonist in Kung Fu Panda 2, a ruthless and power-obsessed warlord who seeks to conquer China using deadly fireworks-based weaponry.
-
C.
Khương Đình Ward
Khương Đình Ward is an urban administrative subdivision of Thanh Xuân District in Hanoi, Vietnam.
-
D.
Han Lue
Han Lue is a laid-back, skilled street racer and heist crew member in the Fast & Furious franchise, known for his calm demeanor, drifting talent, and constant snacking.
-
E.
Shu Chien
Shu Chien is a renowned Chinese-American physiologist and bioengineer recognized for pioneering contributions to cardiovascular biomechanics and microcirculation research.
- 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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c2b543c81909b82bc478d579e0b |
completed | March 31, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe0e0b2748190930c22c6157d1b07 |
completed | March 31, 2026, 2:57 p.m. |
Created at: March 30, 2026, 5:15 p.m.