Triple
T9851182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ingo |
E239471
|
entity |
| Predicate | isDiminutiveOf |
P456
|
FINISHED |
| Object | Ingomar |
E824953
|
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: Ingomar | Statement: [Ingo, isDiminutiveOf, Ingomar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ingomar Context triple: [Ingo, isDiminutiveOf, Ingomar]
-
A.
Ingomar
chosen
Ingomar is a masculine given name of Germanic origin, used as a variant form of the name Ingo.
-
B.
Zelmo
Zelmo is a masculine given name most notably associated with American basketball Hall of Famer Zelmo Beaty.
-
C.
Mulgarath
Mulgarath is the primary shapeshifting ogre antagonist in The Spiderwick Chronicles, seeking to obtain a powerful field guide to dominate the magical world.
-
D.
Alvarus
Alvarus is a Latinized variant of the given name Alvar, historically used in medieval and ecclesiastical contexts.
-
E.
Dorohusk
Dorohusk is a village in eastern Poland near the Ukrainian border, known as an important road and rail border crossing point between the two countries.
- 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3742fe48190bae6e6d828a9cc0d |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1e4349a50819084ee1fcd067a082b |
completed | April 5, 2026, 4:25 a.m. |
Created at: March 30, 2026, 8:34 p.m.