Triple
T66963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giza railway station |
E1334
|
entity |
| Predicate | languageOfSignage |
P4196
|
FINISHED |
| Object | Arabic |
—
|
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: Arabic | Statement: [Giza railway station, languageOfSignage, Arabic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfSignage Context triple: [Giza railway station, languageOfSignage, Arabic]
-
A.
languageOfCeremony
Indicates the language in which a ceremony is conducted or officially performed.
-
B.
hasSignificantLanguage
Indicates that an entity possesses a language that plays an important or primary role in its communication, identity, or functioning.
-
C.
officialLanguage
Indicates that a particular language has been formally designated by an authority as the official language used for government, legal, or administrative purposes in a given jurisdiction.
-
D.
languageOfRecords
Indicates the language in which the records are written or maintained.
-
E.
languageOfSources
Indicates that the specified language is the language in which the referenced sources or source materials are expressed.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a2509b5a088190bb9d2b650aeb8bca |
completed | Feb. 28, 2026, 2:19 a.m. |
| PD | Predicate disambiguation | batch_69a24ea749788190bc17865171ff909a |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a2509a1c088190b4afa3045455709a |
completed | Feb. 28, 2026, 2:19 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.