Triple
T33871410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janet Hodgson |
E868216
|
entity |
| Predicate | ageDuringEnfieldCase |
P67527
|
FINISHED |
| Object | young girl |
—
|
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: young girl | Statement: [Janet Hodgson, ageDuringEnfieldCase, young girl]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ageDuringEnfieldCase Context triple: [Janet Hodgson, ageDuringEnfieldCase, young girl]
-
A.
ageAtTimeOfShooting
Indicates the age a person or entity was at the specific time when the shooting event occurred.
-
B.
ageAtEnlistment
Indicates the age a person was when they enlisted in a service, organization, or role.
-
C.
estimatedAgeYears
Indicates the approximate number of years assigned as an entity’s age based on estimation rather than exact measurement.
-
D.
ageDuringNarration
chosen
Indicates that an entity has a specified age at the time when the described narrative or event is taking place.
-
E.
ageAtIntroduction
Indicates the age an entity had at the time it was first introduced or presented in a given context.
- 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_69f34995029081909ede0f7df73d1a5e |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f700a872c88190a7987b2f9798986e |
completed | May 3, 2026, 8 a.m. |
| PD | Predicate disambiguation | batch_69f6fc5a4f7881909324eb3c20ca96f1 |
completed | May 3, 2026, 7:42 a.m. |
Created at: May 1, 2026, 1:47 a.m.