Triple
T10334227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hyacinth |
E242957
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Jacinto |
E68983
|
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: Jacinto | Statement: [Hyacinth, hasVariant, Jacinto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jacinto Context triple: [Hyacinth, hasVariant, Jacinto]
-
A.
Jacinto
chosen
Jacinto is the cruel caretaker and primary antagonist in Guillermo del Toro’s gothic horror film "The Devil’s Backbone."
-
B.
Flor silvestre
Flor silvestre is a classic Mexican film best known for featuring iconic actress Dolores del Río in a leading role.
-
C.
Reseda
Reseda is a residential neighborhood in the central San Fernando Valley region of Los Angeles, known for its suburban character and diverse community.
-
D.
Mirabilis
Mirabilis is a genus of flowering plants best known for ornamental species like the four o'clock flower, valued for their colorful, fragrant blooms that often open in the late afternoon.
-
E.
Mirabilis
Mirabilis is an Israeli software company best known for creating the pioneering instant messaging service ICQ in the 1990s.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4dfc366b481909c49f199892e9d42 |
completed | April 7, 2026, 10:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d75054515081908240f985f8b6e2df |
completed | April 9, 2026, 7:08 a.m. |
Created at: April 6, 2026, 11:53 a.m.