Triple
T10246786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Centro Médico |
E240234
|
entity |
| Predicate | hasStationLogo |
P87191
|
FINISHED |
| Object | stylized caduceus on white background |
—
|
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: stylized caduceus on white background | Statement: [Centro Médico, hasStationLogo, stylized caduceus on white background]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStationLogo Context triple: [Centro Médico, hasStationLogo, stylized caduceus on white background]
-
A.
hasStationIcon
chosen
Indicates that an entity is associated with a specific icon used to visually represent a station.
-
B.
hasOfficialLogo
Indicates that one entity is the formally recognized logo or emblem representing another entity.
-
C.
ownedStationsBranding
Indicates that a particular branding is used by stations that are owned by a specified entity.
-
D.
hasLogoText
Indicates that an entity’s logo includes specific textual content or wording.
-
E.
hasFormerLogo
Indicates that an entity is associated with a logo it used in the past but no longer uses.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d328272c8190a3548d7f7f38cfc4 |
completed | April 7, 2026, 9:49 a.m. |
| PD | Predicate disambiguation | batch_69d4d1ebd6c88190a1f3f4a72a99d6fe |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:27 a.m.