Triple
T6986348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Governor-General’s House, Lahore |
E161971
|
entity |
| Predicate | hasLocalNameScript |
P6353
|
FINISHED |
| Object | Arabic script |
—
|
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 script | Statement: [Governor-General’s House, Lahore, hasLocalNameScript, Arabic script]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalNameScript Context triple: [Governor-General’s House, Lahore, hasLocalNameScript, Arabic script]
-
A.
hasLocalName
chosen
Indicates that an entity is known by a specific name or designation within a particular local language, script, or regional context.
-
B.
hasLocalNameFor
Indicates that one entity serves as the local or context-specific name or label used to refer to another entity.
-
C.
hasUnicodeScript
Indicates that a character or text element belongs to a specific Unicode script category (such as Latin, Cyrillic, or Han).
-
D.
hasLocalCharacter
Indicates that something possesses qualities, features, or significance that are specific to a particular locality or region.
-
E.
hasNoWidelyUsedLocalName
Indicates that the entity does not have a commonly used or widely recognized name in the local language or region.
- 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_69c68855dc0481909b4c7e9e9ed273db |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbbd926c8190a8b60527bd553fa3 |
completed | March 27, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c4a18881908d267137daed828b |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:32 p.m.