Triple
T14171576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ingrid Mössinger |
E351219
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ingrid |
E108797
|
NE 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: Ingrid | Statement: [Ingrid Mössinger, givenName, Ingrid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ingrid Context triple: [Ingrid Mössinger, givenName, Ingrid]
-
A.
Ingrid
chosen
Ingrid is a feminine given name of Scandinavian origin that has been borne by several notable figures, including the Swedish actress Ingrid Bergman.
-
B.
INGRID
INGRID is a near detector of the T2K long-baseline neutrino experiment, designed to monitor the neutrino beam’s direction and intensity.
-
C.
Ingeborg
Ingeborg is a feminine given name of Germanic origin, commonly used in German-speaking and Scandinavian countries.
-
D.
Inger
Inger is a central female character in Knut Hamsun’s novel "Growth of the Soil," representing the hardships and moral complexities of rural Norwegian life.
-
E.
Nina
Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61b5dcbc8190b0cfcce5e6c6d582 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf808e6088190a607903be0f2adc7 |
completed | May 7, 2026, 8:37 p.m. |
Created at: April 10, 2026, 1:01 a.m.