Triple
T14006733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hedwig Robinson |
E336967
|
entity |
| Predicate | hasSurgicalTrauma |
P3816
|
FINISHED |
| Object | botched sex-change operation |
—
|
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: botched sex-change operation | Statement: [Hedwig Robinson, hasSurgicalTrauma, botched sex-change operation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSurgicalTrauma Context triple: [Hedwig Robinson, hasSurgicalTrauma, botched sex-change operation]
-
A.
hasSurgery
Indicates that a surgical procedure is performed on or undergone by an entity.
-
B.
hasInjuries
chosen
Indicates that an entity has sustained one or more physical or bodily injuries.
-
C.
hadProcedure
Indicates that a subject underwent or received a specific medical or clinical procedure.
-
D.
wasWoundedIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or conflict.
-
E.
hasApproximateNumberOfWounds
Indicates that an entity has a number of wounds that is known only approximately rather than as an exact count.
- F. None of above.
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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ed327d88190a53af5768468a8eb |
completed | April 14, 2026, 12:10 p.m. |
| PD | Predicate disambiguation | batch_69dd465dfbc4819090d8c61fd572d35f |
completed | April 13, 2026, 7:39 p.m. |
Created at: April 9, 2026, 10:19 p.m.