Triple
T52798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chrissy Teigen |
E1036
|
entity |
| Predicate | hasSurgery |
P3280
|
FINISHED |
| Object | cosmetic breast surgery (breast implants and later removal) |
—
|
LITERAL 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: cosmetic breast surgery (breast implants and later removal) | Statement: [Chrissy Teigen, hasSurgery, cosmetic breast surgery (breast implants and later removal)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSurgery Context triple: [Chrissy Teigen, hasSurgery, cosmetic breast surgery (breast implants and later removal)]
-
A.
hasReception
Indicates that an entity hosts, includes, or is associated with a reception event (such as a formal gathering or welcoming function).
-
B.
famousOperation
Indicates that an operation is widely recognized or renowned, typically due to its historical, cultural, or technical significance.
-
C.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
D.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
E.
hasAwarded
Indicates that one entity has given or conferred an award to another entity.
- F. None of above. chosen
Provenance (4 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24c709c248190bcd442c8d508e48c |
completed | Feb. 28, 2026, 2:01 a.m. |
| PD | Predicate disambiguation | batch_69a24ac3c8dc819099849023bdaa35a9 |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24c6ff0588190b0fd864da9aa8569 |
completed | Feb. 28, 2026, 2:01 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.