Triple
T4700705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Neutron (1932 paper) |
E104261
|
entity |
| Predicate | mainSubject |
P3
|
FINISHED |
| Object |
neutron
A neutron is a neutral subatomic particle found in atomic nuclei, playing a key role in nuclear stability and reactions.
|
E460130
|
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: neutron | Statement: [The Neutron (1932 paper), mainSubject, neutron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: neutron Context triple: [The Neutron (1932 paper), mainSubject, neutron]
-
A.
Neutor
Neutor is a historic city gate in Nuremberg, Germany, forming part of the city’s preserved medieval fortifications.
-
B.
Niwt-Imn
Niwt-Imn is the ancient Egyptian name for the city later known as Thebes, a major religious and political center along the Nile.
-
C.
Neutrino
A neutrino is an extremely light, electrically neutral elementary particle that interacts only via the weak nuclear force and gravity, making it very difficult to detect.
-
D.
Sciama
Sciama is a surname most notably associated with British physicist Dennis Sciama, a key mentor to several leading cosmologists.
-
E.
NEOB
NEOB is a U.S. federal government office building in Washington, D.C., that houses various agencies and staff of the Executive Office of the President.
- 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: neutron Triple: [The Neutron (1932 paper), mainSubject, neutron]
Generated description
A neutron is a neutral subatomic particle found in atomic nuclei, playing a key role in nuclear stability and reactions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: neutron Target entity description: A neutron is a neutral subatomic particle found in atomic nuclei, playing a key role in nuclear stability and reactions.
-
A.
Neutor
Neutor is a historic city gate in Nuremberg, Germany, forming part of the city’s preserved medieval fortifications.
-
B.
Niwt-Imn
Niwt-Imn is the ancient Egyptian name for the city later known as Thebes, a major religious and political center along the Nile.
-
C.
Neutrino
A neutrino is an extremely light, electrically neutral elementary particle that interacts only via the weak nuclear force and gravity, making it very difficult to detect.
-
D.
Sciama
Sciama is a surname most notably associated with British physicist Dennis Sciama, a key mentor to several leading cosmologists.
-
E.
NEOB
NEOB is a U.S. federal government office building in Washington, D.C., that houses various agencies and staff of the Executive Office of the President.
- 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_69bd43e9b88481908582103dcadff3d9 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd63cd447081908120ee1691009982 |
completed | March 20, 2026, 3:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be03ca24848190aa7df32472647cae |
completed | March 21, 2026, 2:34 a.m. |
| NEDg | Description generation | batch_69be04513ec081909d38f33de795402b |
completed | March 21, 2026, 2:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be051027c88190a4f24ed5cb17b605 |
completed | March 21, 2026, 2:40 a.m. |
Created at: March 20, 2026, 1:17 p.m.