Triple
T7212776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamed |
E149448
|
entity |
| Predicate | hasVariantSpelling |
P457
|
FINISHED |
| Object | Hammad |
E345173
|
NE 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: Hammad | Statement: [Hamed, hasVariantSpelling, Hammad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hammad Context triple: [Hamed, hasVariantSpelling, Hammad]
-
A.
Hammad
chosen
Hammad is a character in the novel "Falling Man," which explores the aftermath of the September 11 attacks.
-
B.
Qasim
Qasim is a central character in Naguib Mahfouz’s novel "Children of Gebelawi," representing a modern, socially conscious figure modeled on the Prophet Muhammad within the book’s allegorical retelling of religious history.
-
C.
Hammad ibn Abi Sulayman
Hammad ibn Abi Sulayman was an early Kufan Islamic jurist and prominent scholar of Hanafi legal thought, best known as the principal teacher of Imam Abu Hanifa.
-
D.
Umara
Umara is the plural form of the Arabic name or title "Amir," commonly used to refer to multiple rulers or princes.
-
E.
Hamed
Hamed is a masculine given name commonly used in Arabic-speaking and Muslim-majority cultures.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c687eca814819095abb52316b1af80 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e98b61448190add3624a818fdc7b |
completed | March 27, 2026, 8:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7db092ed481908f478860418eb0bd |
completed | March 28, 2026, 1:43 p.m. |
Created at: March 27, 2026, 2:53 p.m.