Triple
T36807837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franja de Ponent |
E909503
|
entity |
| Predicate | hasSignageIssues |
P196608
|
FINISHED |
| Object | debates over bilingual signage |
—
|
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: debates over bilingual signage | Statement: [Franja de Ponent, hasSignageIssues, debates over bilingual signage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSignageIssues Context triple: [Franja de Ponent, hasSignageIssues, debates over bilingual signage]
-
A.
hasSignage
Indicates that appropriate signs or visual markers are present to convey information, directions, warnings, or identification related to the associated entity.
-
B.
hasSignageIn
Indicates that appropriate signs or signage for an entity are present or installed within a specified location or area.
-
C.
hasSignageType
Indicates the specific category or kind of signage associated with an object, location, or entity.
-
D.
hasSignageName
Indicates that an entity has a specific name or label as it appears on its physical signage.
-
E.
hasSignageLetter
Indicates that an object or location bears signage containing a specific letter or set of letters.
- F. None of above. chosen
Provenance (4 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_69f76e7cbbf48190891227b14d041139 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fe5d58c3e48190910aa3c23485e2c4 |
completed | May 8, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69fe5c92090c8190bcfa412c0a3619df |
completed | May 8, 2026, 9:58 p.m. |
| PDg | Predicate description generation | batch_69fe5d580d7c819097cb18d438b01d08 |
completed | May 8, 2026, 10:02 p.m. |
Created at: May 3, 2026, 4:13 p.m.