Triple
T3346504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Take Ionescu |
E70386
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Ionescu
Ionescu is a common Romanian surname borne by numerous notable figures in fields such as literature, music, and sports.
|
E350398
|
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: Ionescu | Statement: [Take Ionescu, familyName, Ionescu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ionescu Context triple: [Take Ionescu, familyName, Ionescu]
-
A.
Avramescu
Avramescu is a Romanian surname most notably associated with Gheorghe Avramescu, a Romanian general during World War II.
-
B.
Ion Țuculescu
Ion Țuculescu was a Romanian painter and biologist known for his vibrant, expressionist works that drew heavily on Romanian folklore and traditional motifs.
-
C.
Lucian Mureșan
Lucian Mureșan is a Romanian cardinal and Major Archbishop who leads the Romanian Greek Catholic Church.
-
D.
Dumitru Erhan
Dumitru Erhan is a computer scientist and machine learning researcher known for influential work in deep learning and neural networks.
-
E.
Donca Steriade
Donca Steriade is a prominent linguist and phonologist known for influential work on segmental phonology, syllable structure, and the theory of lexical and positional neutralization.
- 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: Ionescu Triple: [Take Ionescu, familyName, Ionescu]
Generated description
Ionescu is a common Romanian surname borne by numerous notable figures in fields such as literature, music, and sports.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ionescu Target entity description: Ionescu is a common Romanian surname borne by numerous notable figures in fields such as literature, music, and sports.
-
A.
Avramescu
Avramescu is a Romanian surname most notably associated with Gheorghe Avramescu, a Romanian general during World War II.
-
B.
Ion Țuculescu
Ion Țuculescu was a Romanian painter and biologist known for his vibrant, expressionist works that drew heavily on Romanian folklore and traditional motifs.
-
C.
Lucian Mureșan
Lucian Mureșan is a Romanian cardinal and Major Archbishop who leads the Romanian Greek Catholic Church.
-
D.
Dumitru Erhan
Dumitru Erhan is a computer scientist and machine learning researcher known for influential work in deep learning and neural networks.
-
E.
Donca Steriade
Donca Steriade is a prominent linguist and phonologist known for influential work on segmental phonology, syllable structure, and the theory of lexical and positional neutralization.
- 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_69ad85a405e48190b6e68de7cf9f319e |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb1f4ff888190bf14b9b7fbe9bcee |
completed | March 8, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b32522cbc88190b965087a580d7acf |
completed | March 12, 2026, 8:42 p.m. |
| NEDg | Description generation | batch_69b325a0de7c8190a33ae611b8450b5a |
completed | March 12, 2026, 8:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b326862e848190bf1bea74b6ab0b36 |
completed | March 12, 2026, 8:48 p.m. |
Created at: March 8, 2026, 3:12 p.m.