Triple
T16241973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al-Waziriya campus |
E394269
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Waziriya district
Waziriya district is an area of Baghdad, Iraq, known for hosting educational institutions such as the Al-Waziriya campus.
|
E1201937
|
NE FINISHED |
How this triple was built (4 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: Waziriya district | Statement: [Al-Waziriya campus, locatedIn, Waziriya district]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Waziriya district Context triple: [Al-Waziriya campus, locatedIn, Waziriya district]
-
A.
Montaza district
Montaza district is a coastal area in Alexandria, Egypt, known for its expansive royal gardens, beaches, and historic palaces.
-
B.
Sheema District
Sheema District is an administrative district in southwestern Uganda known for its predominantly rural communities and agricultural-based economy.
-
C.
Mizan District
Mizan District is an administrative district located within Zabul Province in southern Afghanistan.
-
D.
Rashidan District
Rashidan District is an administrative district in central Afghanistan known for its rural communities within Ghazni Province.
-
E.
Hassan district
Hassan district is an administrative region in the state of Karnataka, India, known for its rich Hoysala-era temple architecture and agricultural prominence.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Waziriya district Triple: [Al-Waziriya campus, locatedIn, Waziriya district]
Generated description
Waziriya district is an area of Baghdad, Iraq, known for hosting educational institutions such as the Al-Waziriya campus.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Waziriya district Target entity description: Waziriya district is an area of Baghdad, Iraq, known for hosting educational institutions such as the Al-Waziriya campus.
-
A.
Montaza district
Montaza district is a coastal area in Alexandria, Egypt, known for its expansive royal gardens, beaches, and historic palaces.
-
B.
Sheema District
Sheema District is an administrative district in southwestern Uganda known for its predominantly rural communities and agricultural-based economy.
-
C.
Mizan District
Mizan District is an administrative district located within Zabul Province in southern Afghanistan.
-
D.
Rashidan District
Rashidan District is an administrative district in central Afghanistan known for its rural communities within Ghazni Province.
-
E.
Hassan district
Hassan district is an administrative region in the state of Karnataka, India, known for its rich Hoysala-era temple architecture and agricultural prominence.
- F. None of above. chosen
Provenance (5 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2455eeb4c81909066a8af78329ef3 |
completed | April 17, 2026, 2:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000edf64a88190a9dd0c591c742977 |
completed | May 10, 2026, 4:51 a.m. |
| NEDg | Description generation | batch_6a00108174ac8190b3c421b115b7190e |
completed | May 10, 2026, 4:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0010f40d6081909927e8281ab17580 |
completed | May 10, 2026, 5 a.m. |
Created at: April 10, 2026, 5:04 a.m.