Triple
T10195490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomasino |
E238151
|
entity |
| Predicate | hasPluralForm |
P5088
|
FINISHED |
| Object | Tomasinos |
E238151
|
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: Tomasinos | Statement: [Tomasino, hasPluralForm, Tomasinos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tomasinos Context triple: [Tomasino, hasPluralForm, Tomasinos]
-
A.
Tomasino
chosen
Tomasino is the colloquial term used to refer to students of the University of Santo Tomas in the Philippines.
-
B.
Vinantes
Vinantes is a small French commune located in the Seine-et-Marne department in the Île-de-France region in north-central France.
-
C.
Tiendesitas
Tiendesitas is a popular shopping and lifestyle complex in Pasig, Metro Manila, known for its Filipino-themed architecture, handicrafts, food, and live entertainment.
-
D.
Toma
Toma is a major Mande language spoken primarily in Guinea and neighboring West African countries.
-
E.
Toma
Toma is a traditional semi-hard cow’s milk cheese from Italy’s Piedmont region, known for its mild, buttery flavor and smooth, elastic texture.
- 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdedc9360081908dd73e36022a3dfd |
completed | April 2, 2026, 4:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d317dbfb7c819087aafc7161b14707 |
completed | April 6, 2026, 2:18 a.m. |
Created at: March 30, 2026, 9:13 p.m.